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Titlebook: Concepts of Soft Computing; Fuzzy and ANN with P Snehashish Chakraverty,Deepti Moyi Sahoo,Nisha Ran Textbook 2019 Springer Nature Singapore

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發(fā)表于 2025-3-23 11:47:38 | 只看該作者
12#
發(fā)表于 2025-3-23 16:55:51 | 只看該作者
: The Intermediate Value Theorem,terval eigenvalue problems.?Chapter 8 dealt with solving interval system of linear equations using interval analysis and in this chapter, we have focused on solving?eigenvalue problems having interval parameters.
13#
發(fā)表于 2025-3-23 18:42:59 | 只看該作者
Raymond F. Dickman,Peter FletcherN) because the processing is similar to the human brain. An ANN is composed of large number of highly interconnected processing elements called the neurons which work in union to solve different problems.?This chapter contains preliminaries of Artificial Neural Network,?types of neural network and?i
14#
發(fā)表于 2025-3-23 23:58:30 | 只看該作者
Shape theory and covering spaces,euron allows binary activation (1 ON or 0 OFF), i.e., it either fires with an activation 1 or does not fire with an activation of 0. If w > 0, then the connected path is said to be excitatory else it is known as inhibitory. Excitatory connections have positive weights and inhibitory connections have
15#
發(fā)表于 2025-3-24 06:20:47 | 只看該作者
,équivalence algébrique-analytique,at if two interconnected neurons are both “on” at the same time, then the weight between them should be increased. Hebbian network is a single layer neural network which consists of one input layer with many input units and one output layer with one output unit. This architecture is usually used for
16#
發(fā)表于 2025-3-24 09:14:33 | 只看該作者
Topos anneles et schemas relatifsrable. Single layer perceptron consists of one input layer with one or many input units and one output layer with one or many output units.?The present chapter describes about the single layer perceptron and its learning algorithm. The chapter also includes different Matlab program for calculating o
17#
發(fā)表于 2025-3-24 11:56:40 | 只看該作者
Toposes, Algebraic Geometry and Logic is a forward flow of information and no feedback between the layers. Such type of network is known as feedforward networks.?This chapter discusses?feedforward neural network, delta learning rule.?Error back propagation algorithm for unipolar and bipolar activation function are included in this chap
18#
發(fā)表于 2025-3-24 18:02:40 | 只看該作者
19#
發(fā)表于 2025-3-24 22:51:15 | 只看該作者
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發(fā)表于 2025-3-25 00:53:32 | 只看該作者
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