1. This paper proposes a novel reactive distributed artificial intelligence (dynamic) using immune networks and other soft computing methods. Extended soft computing is defined by adding immune networks, chaos theory, and wavelets to conventional soft computing.
2. A novel fuzzy neural network (general parameter radial based function neural network) is developed to use for communication among agents in immune networks. The general parameter method is extended to an adaptive structured genetic algorithm to obtain much faster convergence.
3. This developed fuzzy neural network is extended to a high performance radial basis function neural network using an adaptive structure genetic algorithm, and is applied to optimize Ishiguro's immune network reactive distributed artificial intelligence.
Hand Gesture Recognition using OpenCV and Pythonijtsrd
Hand gesture recognition system has developed excessively in the recent years, reason being its ability to cooperate with machine successfully. Gestures are considered as the most natural way for communication among human and PCs in virtual framework. We often use hand gestures to convey something as it is non verbal communication which is free of expression. In our system, we used background subtraction to extract hand region. In this application, our PCs camera records a live video, from which a preview is taken with the assistance of its functionalities or activities. Surya Narayan Sharma | Dr. A Rengarajan "Hand Gesture Recognition using OpenCV and Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38413.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38413/hand-gesture-recognition-using-opencv-and-python/surya-narayan-sharma
My presentation gives a brief overview about soft computing and it's concepts. Such as..Neural networks, Machine learning, Artificial Intelligence etc...
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
This presentation shows the impact of GPU computing on cognitive robotics by showing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amount of computational power, which was until recently provided mostly by standard CPU processors. However, CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. However, this impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This presentation shows several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity, which enabled conducting the novel experiments described herein.
Hand Gesture Recognition using OpenCV and Pythonijtsrd
Hand gesture recognition system has developed excessively in the recent years, reason being its ability to cooperate with machine successfully. Gestures are considered as the most natural way for communication among human and PCs in virtual framework. We often use hand gestures to convey something as it is non verbal communication which is free of expression. In our system, we used background subtraction to extract hand region. In this application, our PCs camera records a live video, from which a preview is taken with the assistance of its functionalities or activities. Surya Narayan Sharma | Dr. A Rengarajan "Hand Gesture Recognition using OpenCV and Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38413.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38413/hand-gesture-recognition-using-opencv-and-python/surya-narayan-sharma
My presentation gives a brief overview about soft computing and it's concepts. Such as..Neural networks, Machine learning, Artificial Intelligence etc...
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
This presentation shows the impact of GPU computing on cognitive robotics by showing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amount of computational power, which was until recently provided mostly by standard CPU processors. However, CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. However, this impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This presentation shows several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity, which enabled conducting the novel experiments described herein.
A brief survey of approaches to using cognitive science artificial intelligence to achieve goals in both the cognitive science and artificial intelligence fields.
NETWORK LEARNING AND TRAINING OF A CASCADED LINK-BASED FEED FORWARD NEURAL NE...ijaia
Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Stock Prediction Using Artificial Neural Networksijbuiiir1
Accurate prediction of stock price movements is highly challenging and significant topic for investors. Investors need to understand that stock price data is the most essential information which is highly volatile, non-linear, and non-parametric and are affected by many uncertainties and interrelated economic and political factors across the globe. Artificial Neural Networks (ANN) have been found to be an efficient tool in modeling stock prices and quite a large number of studies have been done on it. In this paper ANN modeling of stock prices of selected stocks under BSE is attempted to predict closing prices. The network developed consists of an input layer, one hidden layer and an output layer and the inputs being opening price, high, low, closing price and volume. Mean Absolute Percentage Error, Mean Absolute Deviation and Root Mean Square Error are used as indicators of performance of the networks. This paper is organized as follows. In the first section, the adaptability of ANN in stock prediction is discussed. In section two, we justify the using of ANNs in forecasting stock prices. Section three gives the literature review on the applications of ANNs in predicting the stock prices. Section four gives an overview of artificial neural networks. Section five presents the methodology adopted. Section six gives the simulation and performance analysis. Last section concludes with future direction of the study
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
1. Soft Computing (Immune Networks)
In Artificial Intelligence
Yasuhiko Dote
Muroran Institute of Technology
Mizumoto 27-1, Muroran 050 8585,Japan
dote@,csse.muroran-it.ac.jp
ABSTRACT
iliis paper proposes a novel reactive distnbuted artificial usualh achieve theu tasks i n groups. llcactive a y i t s are sittiatcd
~ntcIiigeiice (dvnaniic)using immune networks and other soft they do not take past e~eiits account. a i d can iiot Ibrcsee rlic
into
Loiiiptitiiig inethods Fusth. extended sot? computing is defined ftiture. Their action is based on hat happens no. ho the ~ C I I ~ ~
In .idding iiiuiiuiie networks and chaos theory including fractal distmzuish situations ui Ilie aorld. on the ~ a vthev resognve
and ivavelet to conventional sott computing which is the fusion or world indexes and react accordingly llius. reacuve agents can not
coinbinatioii of tiizzv systenis.neural networks and genetic plan ahead what they will do But, what can be considered as a
.~lgoritlinis and is suitable to cognitive distnbuted artificial weakness is one of theu strengths because the! do not ha^ to
~ i i ~ c l l i ~ e n(static) Next, a novel fuuv neural net(genera1
ce revise their world model when perturbations chaiige the orld in
parameter radial based function neural network) is developed in an tmepected u a ) Robustness and tatilt toleraiict arc t n o 01 the
order to use it for communication among agents in immune main properties of reactive agent swttiiis. j2 group of r e ; ~ c t ~ w
iictnorhs The geiieral paraineter method is &ended to an agents can coinplete tasks even when one o l them b r d s doun.
adaptive structured genetic algorithm to obtain much faster The loss of one agent does iiot prohibit the coinpietion o l the
convergence rate An unbiasedness criterion using distorter( a whole task, because allocation of roles is achieved locall bv
radial based ftiiiction network i order to optimize parameters
n perception of the enviroiunental needs. 'Thus, reactive ageiit
resultiiy in die reactive distributed artificial intelligence hnd of svstems are considered as v:
e flexible and adaptive because[ I 1
(;MD!i) is applied to better generalization propertes. Then, t h s In this p a p e r ;I nozcaI re;ictive distributrtl ;irtif'i(,i:il
developed I'tvrv neural net is extended to a h g h performance int r llige nrr is proposvd us1 ti g Ish igrii ro's i n i i i i i i ne
1. INTRODUCTION n r t w o r k ;11~[~r(~~1t~Iil'l;in'li:(il ot1ic.r +oft rwmputin?
:inc
I<eactivit is a hehavior-bewd model of activitv,as opposed to approaches In section 11. soft coinpiit ing propoawl I)?
x
the svmbol inanipulation model u.wd in planning.This leads to the L1r.L .i.Zadrh[-i] is r x t r n t l t d by :Ititling rhaos coinpiti ing
iiotioii of cognitive c0st.i e.. the complexity of the over and iinmunr net,work theory. i novel fuzzy neural
,Irchitecture needed to achieve a task Cognitive agents support a n r h v o r k with grneral p;lr;imrt.c-r statistics calculus taking
coinplz architecture which inems that their cognitive cost is advantages of both fiii.z ins arid neural iictnorhs i n section
IiigIi.Copnitive agents have intenml representation of the world 111 In section IV this IS eaciided to a high perfonnoiice radial
l i i ~ l iiiiiist he in adequation with the morld itself T h e process of hasis tiiiiction iieural iitturh using oii adaptive structure genetic
rslating the tiitenial representation and the world is considered as algorithinl5 I close to the seneral parciiiieter iiicthod arid o kind 01'
'I task On the other hand. reactive agents are simple.
~oinple- (iMnH[61. In section V these developed nctorks are applied to
cash to uiiderstwd and do not support intemal representation of optiiiiize Ishiguro's uimiune network reactive distributed artiticial
the world. Ilius. their cognitive cost is low, and tend to what is intelligence.
cognitive economy. the property of being able to perfom
~alled
cvcn complsr actions with simple architectures Because of their 11. EXTENDED SOFT COMPUTING
complexit. cognitive agents are otteii considered as self- Soft coiiiputing is proposed bv I)r I* A./.adch/-ll to coiistrtict
~iitlicicrit the can nork alone or nith a ten other agents.0n die ne generation Ai [ macliiiie .intelligeiicc quatient) and to solve
coiitrm. reackive agcnts need companionshp 'I'hey can iiot work noiiliiiear and inatliematicallv iiimioJelld systems prohlenis
isolated and they usually achieve their tasks in groups. Reactive (tractability) especiallv for cognitive artilicial intelligence In this
agents me companionship. They can not work isolated and thev section by adding chaos coiiiptituig arid iiiuiitiiie network thron.
198 $10.00 0 1998 IEEE
0-7803-4778-1 ,
1382
2. ilii extended soft computing is detined for explaining, what thev s t a r t i n g from t h e same initi;il conrhtions. In t h e first
call. complex svstems(7). hunune networks are promising case. :I coiivcsiit ioniil I2HF" wiis uswl w i t h in~livi~Iu;il
approachos to construct reactive artiticial intelligence[21 and [ 3 ]as adjusting of i t s vciights. I n t h e s;c.c:ontlc:ise. ( ~ l ' - l i l $ l ~ N
Illustnltcd 111 Fig I was simuliitrtl with leiirning iilgorit hili (2) 'l'h~,
Hiinian I~eirig i k e .AI
l vfficirnc? of both mrthorls w e r r ~ o n i i ~ ~ r byd using
r
~~-
Cogniriw t h e following m r a s u r e of convergrncr sprrd
Fuzzy 1)istribur~d
Systern ;I (Stnticl
Fig.1 Soft computing in AI
111. NOVEL FUZZY NEURAL NET
I 'J
I irst. id consider the (iP approach to KHFN weights adjust~ng. Figure2. The simplest G P - R R F N
;s soon iis IIRI,'N IS linear on its eights. the (iP method may
bo impIementc.d in a straightfonxard manner The equation
dcscribiiig (if'-RBFN for a single output network is
U IirrI, 1:
. fisrcl initial v a l u e s of' network we1ght.s: p: 0'
400 800 1
si:;il;ir grnrr;il p a r a m e t e r t,o be adjusted with t h e
fol I ow 1ng algori t hin Figure 3 Im;trning algorithm c:onvt!rgc!nc:o:
ti) conventional IZUCN: 11) [;I'-ltt3FN
1383
3. ~linic.nsioniilit> IeiirninC sprrtl of (;P-RBFS hits
iiicreasril reliitivelj- convent.iona1 RBFN.
IitlFN to be used in adaptive fuzzy system ( A F S ) . in
comnion case. is a s s u m e d to be t.ririned by m e a n s of t h e D(P:
Q=-
iiiiniiiiuiii necessary nunibrr of rules (hidden unit
n u m b e r ) ilc.trrniin:ition a n d adjusting of t h e mean a n d
vwtorh of' iiithvidu:il hidd[,n nodes a s well as
v;iri:incc~ Thereforr. the C: KHFNAFS Jetc,rniines Ih c , " t r u r "
P
thrJir eight5 In t h i s p:iper. t h e simplest CP RBFN fuzz! rulrj n u m b e r b; incrt~iiir~nt;lli!
rwruii iny: I1 1 ~
li;isc~tl :idapt ive fuzzy system for aut.oinatic fuzz!- rule r a k i i l basis fuiiction units ant1 cant inuous est i n i i i t ion
niiinbrr tletemiination is proposed (Fig4). Only t h e of t.he approxlmtition quality through critrriii (4)
nrtworli weights have been a s s u m e d t.o be adjust.ed by evwluat.ion for each fixed GP R B F S structure. T h e
the' (:P algorithm while t h e c r n t r e s a n d widt.hs of unit network t o be determined is the network with 1r:ist
+nsit I V P zon6.s yere ooiiipletel>- tleteriiiined w i t h the v:ilue oi' i, anr! its unit n u m l ~ r rC :issiinic,~l h.
I to
n(,tworli input Gign;il r:iiigr :inil u n i t r,qu;il t o t h e f'uzzy r d c ~
nuiiilwr C I ~c l i t * "s:lnililt~"
i'uzz> .ystc"
Let consider t h e proposed procrdure in c1et.d for r h r
siiiiplest case of t h e (P RBFN AFS Lvith
: sciil;rr input
1 signal
i n p u t slgniil II ( E ' : u := 0 ) iintl linovn nuni1ii.r of
(:aussian units r] (for t h e first stage. y = I ) thr
sensitive zone center coor&n;ites :ire calculiitcd by
, relationship (5).
CP KBFN BASED AFS
- - ___
Figure4. G P RBFN adaptive furLy system
nuiiihrr during riich training rpoch
whrrr. I is ii current unit n u m b e r For y = I ;incl I =I
. "s:iniplr" fuzz!- systrm h a s been present.ed by RBFN
Ui t h I he "unknown" n u m b e r of hidden units (i.e.. fuzzy for rsiiiiiplr. o n r ['tin recrivr (': = 0
rules) Starting t'rom the single-unit-(;P-RBFN. the
nr.twork learning h a s been performed by t h e scii1:ir 3 ) The initial (basic) sensitive zone w i d t h rqu;il i'or all
grneriil piiriiiiieter iirljusting in the Learning netu.orli units I:, c;ilcul;itt.tl as ((5)
blorli ~
l ' r ~ ~ c c ~ ~ l u r ~ T h e stratly st iitr general p a r a m e t e r
( ~ ~ ~it T I
: I ion f<[fl ;ind viiriiince D { P ) have been
c,:ilcul:ittd hy GP Statist,ics Estimnt.or. The
;~pproxiniationquality cnterion (1B) w a s evalutit.etl for
( h p current (:P KRFN st.ructurr. rind decision on
rh;inging o ' nrtwork structure p:ir:iiiirter iicljusting
f
iii t IIP 1,riiriiing Prow[iurr, L~lock. T h e stezicly s t a l e
gr~iii~riil p:ir:iniet<Jr ~ ~ s p e c t ; i t i o n E[P}antl
viiriiince U ( P : have been c;ilculatrd by GP Stat.istics
l%timwt.or. The approx"t.ion quality crit.enon (1:3)
viis n ~ : i I i i ; i t c dfort he current (:P RBFN st.ructure. and
1384
4. IC; p r t o r n i e d biised on input-out,put s a m p l e d;itii In this section. the 1JnbiasediiessCriterion tisiiig Distorter I I K I ) )
;icwrtlingly to the ;iIgorithm ( 2 ) . Simiiltmeousl>- t h r approach( 8 I is used. which has been shon provldlng iiiiproved
features in coiiipare to conveiitioiial methods. such as ~ k a i k e
gc'nrr;il p;ir:iiii~ter iJspwt;it ion E { P ) and viiriiince
Infomiation Cntenon ( A I C ) [ 9 ] and its modification for neural
networks Network Infonnation Criterion (MC) [IO], f i n l n i u m
D[,& :ire estimated with some conventional method.
Descnption Length (MDL)[ I I].
for rs-ample. by t h e movlng average calculation. Let consider the IJCD method application to the GP RBFN A F S
The overall svstein block diagram IS shown ui Fig. 6.5
Both of them are (iP RBFN with a lemiing procedurs llie
same signals are ted uito the network inputs The diiYerelici: I 111
the u a y of the teaclung signal usage While the reaching signill is
fed mto uppa loop without any changes, the lower iietuork is
trained by "distorted", i.e. nonliiirarly traiisfonned, sample d ~ t a
The output of the lower network is also changed hv the
transfoniier of the same transfer function as fir teachins sgiitl
The critenon ol' the iietuork structure optimality is derivedI61.
nhich IS otthe tonnc 7)
%ax (*,; (:2
:1 0
- 60-. C: umax
I .( 'D = 5
/=I
(U ) - I.-? (7 ) ]
(7)
IJiguref,. Definition of GP RBFN basic parameters
where ' ' j-th set (vector) ofthe network input data. 17 overall
c.v;iIii,ii NI :iii(I iiiemorizrtl. variables of the both networks. Tlie structure of the netnork n i t h
the least value of the cntenon 7 1 is assumed to be a soliition ot
the problem
-
:'I ,-
-
'5 ... -
:,
,
,_
-
:!
,
- ...
: '
8 ) The strucciirr of GP RHFN is modified by one inore '.- . . .
[:iiussiaii u n i t recruiting: y = q + l . T h e st.eps 1) - 6)
:I r i a rvl)i.at 6.i I .Y , .VI
The, r r s i i l t of t h e algorif hni 1 ) - 8 ) imp1ement;ition is :I
Fig.6 Determinution of number of units by dibtorter
111 I'uiivtion u n i t s i n c:P IiBFS
The proposed general paaiiieter method in scctioii Ill I
,
$1h i ( . h provic1c.s I he best :ipproxiniating accur:icy In the
again illustrated in Fig.7.. This idear is extended to aii adaptive
car ti is of' fuzxy system theory i t iiieiiiis t h e fuzzy rule
structure genetic algonthm[j]. Geiiotvpe has an adaptive
ii ii m1)c.r clrtc~rminiitionproblriii solution. [8]
structure . The string representation is constructed by two l a y s
1V 1 IJ(il.1 PERFOIWANCE RI3k.N
One is nanied locus l a y . the other .operon l+er as slio!!ii iii
l'he prohiein of the reliahiliiy n1' the denved model is one of thc
IFig 8 For this reprcseiitatioii .live ne genetic o~)er~i~i~iiis
iirs
iiiost iiiipottaiit ones. ansing duruig the identitication task solving
detined in order to scll~orgaiii/t:the siring itriicture and dsvclo1)
Hic model over-titting prevention IS a crucial point tor inam
adaptive genctic change 111 the evolutioiial pro
y l c t i c a l iinplrmeiitations 11s i t WJS discussed ui the preceding
approach bnngs attractive optiiiiiimoii results fbr probizins
sections, there are several approaches to cope with this ditficultv
including (iA-dilticultv.Suice genetic algorithm and chaos
1385
5. Loinputnips are heuristic approaches, they have capabilities of a fashion.Namelv.onlv one antibodv is allowved lo activate and act
creative thinking ivav or evolution its corresponding its action to the ivorld 11' its coiiceiitratioii
H i these techniques the Iuzzv neural net in section III turns Into surpasses the prespecitied the threshhold As shovii in Fig10 . ilic
<I high pcrlbnnancr radial basis fuiictlon neural network concentration of the aiitibodv is influenced b the stimulatioii iuid
!
Fig.7 General parameter method suppression from other antibodies . the stiiiiulation froin antigeii.
String and the dissipation Factor t i c. natural death ). The concentration 01
I-th antibody .which is denoted by a, . is calculated b ( X )
! (I and
0 are the rate of interaction ainong antigens and antibodird.
+. ..... ....
~ a l u elist t i x e d lenzth
+ .~
........ _ - ~ _
Locur libel V V ............
~
.~ ......
V
General Parameter
. . _ ~ ~ -
...
..
..
.. .......
.......
......
N!:eight layer (fixed nominal value)
-. __ ..-
/I;, ... -- .........
Ili,,
-
li:,
.......
II, ... It,;,"
_- r
.---.; * -.:
*.
1 ......
......
? --
i ~ _ _ _
~
:
- ~-~
-~ .
.
.
._. .
~
~
Inputs : blutually Inputs : Mutually
Correlated ' Correlated_ _ -
I
.
. - .... - ...
I .. --
Fig.8 Adaptive string structure o f genetic algorithm N N N
V. SOFT COMPUTLNG I REACTIVE
N tlA,(tvdt=( (L ( XI11 il (1) XI11 ) n i Llll .<I. ( 1 )
DISTRIBUTED ARTIFICIAL J-I 1 1 k 1
INTELLIGENCE I
Is1l l G [ J R O 3 REACTIVE IIISTRIBIJTED ARTIFICIAL X IN:, - 0 111: k. ~ ii: (t) (8)
IN'TEI.Ll(;ENCE WITH M J E NETWORKS[Z] and [i]
MN k=I
i'he detected current situation and competence modules as il. ( t - I ) -1.. (l.rxp(O. 5 - A . ( t ) ) )
.iitigciis and Antibod~es,respzctiveI~ liere N IS die number of antibodies. a i d nil denotc~inatclinis
lo inake a iinonoido(antihody) select a suitable antibodv against ratio hrtneen antibod! I and antigen .m), denotes dcgrce 01
that
ilw wrreiit antigen, it IS highlv important I i o ~ the antibodies disalloance of antibod I for antibod! I 'The first and sccond
arc described .Moreover.it is noticed that the unmunogical tenns of nght hand side denote the stiiiiulatioii and supprzssioti
dntration inecliamsm select an antibody in bottom up manner by from other antibodies, respectively The thrd tenii represents lhr
~ommuiiicating aiiioiig the antibodies. To rwlize the above stimulation from antigen, and the forth tenn thtl natural death
-~ . . ~ _ _ _ ~ - .
rcquireineiits. the descnptioii the description of antibodies are -7zEED
Idiotour
defined as follons The identitv of a specific antibody is generally
. . . ~ ~~
ilcleniiinzd h? the stncture of its paratope and idiotope F i g 5
dcplcts thc represetitation of antibodies As shown iii this tigure.a
pair of precondition action t o paratope .the nuinher of
ll~wllord antibodies and thc degrce ot' disallowance to idiotope
,irc respectively assigned In addition, the structure of paratope is Food Bark Middle Hwkwud
Obsmclr I vtl FW KlEhi
J I ided into four portions: objects, direction,distance, and action. EnrrgY
_ i.cn and c , r .
. - ~
ni>d et,'
For adequate selection of antibodies . one state variable called Fig.9 Represent;rtion of antibodies
concentration is assigned to each antibody. The selection of
;Ilitibodics IS simply carried out i
n a wiimer-take a l b
1386
6. hi order to optimize this reactive distributed artificial intelligence. Heunstic Model Selection Cnterion I king Distorter and
h e deve1opr:d ftiziv neural net is applied to communication Its Application to Detenmiumatioii of the Nuinher oI
aiiioiig agents( antigens and antibodies ) The developed radial Hidden IJIUIS in RBFN', .louiial o t rhr: .lap Soc 01'
hasis function neural net is used to optimize parameters in (8) and Syt.Contr. and Inf.,Vol Il,N0.2,l99X.pp6 1-70
lbr a inetadyaniics whch produces and removes antigens and Y.Dote,"Sott Coniputmg( Immune Networks) 111
ailtibodies to make reactive tables.[f] Artificial Intelligence". Web.site:http-//bik.csse
Muroraim.Japan. I997
VL. CONCLUSION D FhE;hntetov.Y.Dote and M S ShaiMi."Sstriii
1111s paper proposes extaidtxl sott computing to construct 10% Identilicetion bv the (iciieral l'urumeier Netd
cos^ reactive distrihuted artificial intelligence resutmg in excellent Netuorks nith Fuzzy self-or~anizaiion"f'rep. o t the I I"'
decision iiiahng. Table IFAC SVmP on Svsrelll
I shows the comparison of the proposed system vvith fuzzy I 997.~~829-8.34
IdentiIication,Kitak~shu,Japaii,Vol.2,
svstems on decision making. H.Al;aike."A New Look at the Statistical Model
Ideiiti!ication".IEEE Tran. On AC.Vol 19.I974.pp71b
Tirblel Comparison of immune network- 72 3
based with fuzzy reiisoning approach M.Murata.S Yoslukava uid S.Aiiian."Nt.r~orL
Infonnation Cntenoii-l)eieniuiuimg die Nuinher ol'
Iiiiiiiuiic iietnork-bawd T'wn reasoning
Hidden IJiUts for Anilicial Neural Nelnork
t3ottoiii-up decentralized Top-dow~ centralized Model".IEEE Tran. on Neural
IIsplicit uiteraction Implicit interaction
1)viiamir: static Net,Vol.j,No.j, I994,pp865-872.
J kssanen,"A IJniversal Prior tor Integers and
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hitelligence,cdited bv GM.P.O'harc and
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and Y.lJchkava."ki
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Y I Ichil;aa."Constrctioii of a Decentralized
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S stem-Application lo Action Arbitration for an
!
Autonornous Mobile Robot-",The SlCE
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I990 PP I .3b-
IWSC I'Oh.Mtiroraii.Japaii.ApnI27-ZX.
I .37. (I'leiian: Speaker)
IC Ohkura and K.11eda..'Srlf-Orgaiii/;ing of Stnng
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t r TaLeuclu and T Mpos1ii.H Ishihashi and H.Tanaka,"A
1387