Rajendra Akerkar1 and Priti Srinivas Sajja2 1Senior S i Researcher, Vestlandsforsking, Norway R h V tl d f ki N2Associate Professor, Sardar Patel University, India 1 firstname.lastname@example.org 2 email@example.com
NEURO-FUZZY COMPUTING Neural Nets Fuzzy Logic Knowledge K l d Implicit, th I li it the system cannot t t Explicit, E li it verification and ifi ti dRepresentation be easily interpreted or optimization easy and modified (-) efficient (+++) Trains itself by learning from None, None you have to define Trainability data sets (+++) everything explicitly (-) Get “best of both worlds”: f Explicit Knowledge Representation from Fuzzy Logic with Training Algorithms from Neural Nets
The neuro-fuzzy system has the ability of self- learning and it can auto generate rules from data historians other than from expert’s prior knowledge. The i Th aim of the rule extraction is to get rules in the f th l t ti i t t l i th following form from input–output data historians: R(k) : If x1 is A1, x2 is A2 , …., xm is Am then Y is Yk Where R(K) represents the kth rule; e e ep ese ts t e u e; x1, x2, xm are a e input variables; Y is the output variable; A1, A2, Am are input fuzzy sets; and YK is corresponding output fuzzy sets. p y
Each neuron in the input layer, Layer 1, transmits normalized values from the environment to the next layer. Layer 2 is fuzzification layer Neurons in this layer layer. represent fuzzy sets used in the antecedents of fuzzy rules. A fuzzification neuron receives a normalized input from the previous layer and determines the degree to which this input belongs to the neuron s fuzzy set. The neuron’s set activation function of a membership neuron is set to the function that specifies the neuron’s fuzzy set. Layer 3 is fuzzy rule layer Each neuron represents a fuzzy layer. rule. A fuzzy rule neuron receives inputs from the fuzzification neurons of layer 2 that represent fuzzy sets in the rule antecedents. For instance, neuron R1, which corresponds to Rule 1, receives inputs from neurons A1 1 and B1. In a neuro-fuzzy system, intersection can be implemented by the product operator or by Minimum operator.
Layer 4 is output membership layer. Neurons in this layer represent fuzzy sets used in the consequent of fuzzy rules An output membership neuron rules. combines all its inputs by using the fuzzy operation union. The probabilistic OR or Maximum operator can implement this operation. operation Layer 5 is defuzzification layer. Each neuron in this layer represents a single output of the neuro-fuzzy l t i l t t f th f system. It takes the output fuzzy sets clipped by the respective integrated firing strengths and combines them into a single ffuzzy set Ne ro f set. Neuro-fuzzy s stems systems can apply standard defuzzification methods, including techniques like centroid and sum-product composition. composition
Importance of small scale system in economy Though data is available, generalization of rules is difficult Expertise i scarce resource E i is Requirement ◦ Need of effective advisory ◦ User interface ◦ Self-learning
Example rules can be given as follows: If qualification(-0.76), area(-1.6) and dependents(1.9) then opt f more education. d d (1 9) h for d i If qualification(1 6) capital(-1.0), area( 2 9) qualification(1.6), capital( 1 0) area(-2.9) and dependents(0) then opt for job.
Such advisory system can be more generalized and can be considered as a step towards expert system shell with empty knowledge base and an additional editor to document domain knowledge.