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Soft computing aims to mimic human reasoning using techniques like fuzzy logic, neural networks, evolutionary computing and probabilistic reasoning. It has advantages over conventional computing by producing models that are linguistically simple, comprehensible, fast and effective in practice. Soft computing has over 24,000 publications and is used widely in applications such as control systems, business, finance, robotics, science and more.
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Hadoop 2
This document provides a technical introduction to Hadoop, including:
- Hadoop has been tested on a 4000 node cluster with 32,000 cores and 16 petabytes of storage.
- Key Hadoop concepts are explained, including jobs, tasks, task attempts, mappers, reducers, and the JobTracker and TaskTrackers.
- The process of launching a MapReduce job is described, from the client submitting the job to the JobTracker distributing tasks to TaskTrackers and running the user-defined mapper and reducer classes.
Soft computing from net
Soft-computing refers to computational techniques that study and analyze complex phenomena for which conventional methods have not provided low-cost or complete solutions. It includes fuzzy logic, evolutionary computation, neural networks, Bayesian networks, support vector machines, and hybrid systems. Soft-computing techniques are robust, tolerant of imprecise data, and resemble biological processes more than traditional logical techniques. They provide useful approximations to intractable problems rather than exact solutions.
Nis1
Soft computing aims to mimic human reasoning using techniques like fuzzy logic, neural networks, evolutionary computing and probabilistic reasoning. It has advantages over conventional computing by producing models that are more human-like, intuitive, fast and effective in practice. Soft computing has over 24,000 publications and a wide range of applications including control systems, business, finance, robotics, science and more.
Neural network
The document discusses artificial neural networks and the backpropagation algorithm. It provides an overview of ANN structure and learning process. Specifically, it discusses using ANN to control an autonomous vehicle, the backpropagation algorithm which aims to minimize error, and an example of an ANN learning a simple identity function.
Nn devs
This document introduces neural networks and neuro-DEVS. It defines artificial neural networks and provides examples of single neuron and multi-layer network structures. It describes different types of neural networks including perceptrons, multi-layer perceptrons, backpropagation networks, Hopfield networks, and Kohonen feature maps. It discusses areas where neural networks can be useful and their limitations. It outlines the advantages of using neural networks and describes three main applications. It provides an overview of the neuro-atomic model and its use in DEVS simulations, giving an example of a solar energetic system model that uses a neural network as a sub-component.
L005.neural networks
The document provides an overview of artificial neural networks, including:
1) Biological neural networks can learn patterns and generalize, similar to artificial neural networks. Pigeons can distinguish paintings by artist with high accuracy.
2) Artificial neural networks are modeled after biological neurons and synapses. Feedforward networks use backpropagation to train weights and minimize error through multiple iterations.
3) Recurrent networks allow bidirectional information flow and memory over time. Elman networks and Hopfield networks are examples used for language processing and content-addressable memory.
Ch03
This document discusses fuzzy rules and fuzzy reasoning. It covers the extension principle, fuzzy relations, fuzzy if-then rules, the compositional rule of inference, and fuzzy reasoning using single and multiple rules with single and multiple antecedents. Methods like max-min and max-product composition are presented for combining fuzzy relations. Linguistic variables and terms that take linguistic values like "old" are also introduced.
Ch02
- The document outlines key concepts in fuzzy set theory including membership functions, set-theoretic operations, and T-norms and T-conorms (fuzzy intersection and union). It discusses basic definitions like fuzzy complements and extensions to two dimensions.
- Membership functions can take various shapes like triangular, trapezoidal, bell-shaped, and Gaussian and are used to characterize fuzzy sets.
- T-norms and T-conorms provide a way to model fuzzy intersection and union operations and must satisfy properties like monotonicity and commutativity. Common examples include minimum, product, and maximum functions.
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- 3. SC: a user-friendly interface VAN-00
- 15. SC applications: robotics VAN-00 Fukuda’s lab Joseph F. Engelberger We are proud to announce that the HelpMate Robotic Courier has been acquired by Pyxis Corporation . Entertainment robot AIBO
- 17. (Neuro)-fuzzy system construction VAN-00 Training data Experts Fuzzy rules (SOM, c-means etc.) Control data System evaluation (errors) Tuning (NN) New system
- 19. Model construction (trad. rules ) VAN-00 If 0 < x<1, then y=1 If 1 < x<2, then y=0.99 : If 8 < x < 10, then y=0 If 0 < x<1, then y=f(x) If 1 < x<2, then y=g(x) : If 8 < x < 10, then y=h(x) - Rule for each input. => Large rule bases. - Only one rule is fired for each input. - Coarse models.
- 20. Model construction (SC/fuzzy) VAN-00 If x 0, then y 1 If x 5, then y 0.5 If x 10, then y 0 - Approximate values - Rules only describe typical cases (no rule for each input). => Small rule bases. - A group of rules are partially fired simultaneously.