Pattern Recognition using Neural and Functional Networks by Vasantha Kalyani David
By Vasantha Kalyani David
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Extra info for Pattern Recognition using Neural and Functional Networks
17). 29). Fuzzy ART fast learning and complement coding gives the jth category rectangle Rj that includes all those fit vectors a in the fuzzy unit square that have activated category j without reset. The weight vector wj equals (Λj a, (Vj a)c ) as seen in Fig. 6. 12 Fuzzy ART Stable Category Learning A Fuzzy ART system with complement coding, fast learning, and constant vigilance forms categories that converge to limits in response to any sequence of analog or binary input vectors. Category boxes grow in each dimension but never shrink.
Complement coding in neurobiological terms uses both on-cells and off cells to represent an input pattern and preserves individual feature amplitudes while normalizing the total on-cell / off-cell activity. The features that are critically present in category exemplars are encoded by the on-cell portion of a prototype. The off-cell portion encodes features that are critically absent. The features that are sometimes present and sometimes absent in both on-cell and off-cell portions of a prototype are encoded as uninformative by the small weights.
Willems and Rooda  translated the job shop scheduling problem into a linear format and then mapped it into an appropriate neural network structure to obtain a solution. Hopfield and Tank  led the way in using the neural network to solve optimization problems. The basic operation of the Hopfield neural network co-operatively decides neuron output state information based on the state input information from a community of neurons. The neurons apply this information to drive the network to achieve convergence.