Non-Linear Feedback Neural Networks: VLSI Implementations by Mohd. Samar Ansari PDF

By Mohd. Samar Ansari

ISBN-10: 8132215621

ISBN-13: 9788132215622

ISBN-10: 813221563X

ISBN-13: 9788132215639

This e-book goals to give a possible substitute to the Hopfield Neural community (HNN) version for analog computation. it's renowned the traditional HNN suffers from difficulties of convergence to neighborhood minima, and requirement of a giant variety of neurons and synaptic weights. for this reason, more desirable ideas are wanted. The non-linear synapse neural community (NoSyNN) is one such risk and is mentioned intimately during this e-book. This e-book additionally discusses the functions in computationally extensive initiatives like graph coloring, score, and linear in addition to quadratic programming. the cloth within the publication turns out to be useful to scholars, researchers and academician operating within the quarter of analog computation.

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The Hopfield network, as described in [2–5], comprises of a fully interconnected system of n computational elements or neurons. The strength of the connection, or weight, between neuron i and neuron j is determined by Wi j , which may be positive or negative depending on whether the neurons act in an excitatory or inhibitory manner. The internal state of each neuron u i is equivalent to the weighted sum of the external states of all connecting neurons. The external state of neuron i is given by vi , with −1 ≤ vi ≤ 1.

To overcome such a situation, a penalty parameter can be increased to force its associated term to be minimized, but this generally has the unwanted effect of causing other terms to be increased. The solution to this trade-off problem is to find the optimal values of the penalty parameters that balance the terms of the energy function and ensure that each term is minimized. Only then will the constraint terms be zero (a feasible solution), and the objective function be also minimized (a “good” solution).

1) tests the circuit for both negative numbers. The network was also tested for numbers which were very small in magnitude and also very close together. 406. This serves to verify that the NOSYNN based neural network for ranking is indeed capable of providing correct sorting for the numbers applied as initial conditions to the network. 1 are shown in Fig. 8 from where it can be observed that the circuit takes a time of the order of tens of microseconds to correctly sort the two numbers and provide steady state neuronal output voltages.

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Non-Linear Feedback Neural Networks: VLSI Implementations and Applications by Mohd. Samar Ansari

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