Process neural network
HE XIN GUI
Sold by liu xing, Nanjing, JS, China
AbeBooks Seller since April 7, 2009
New - Soft cover
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Add to basketSold by liu xing, Nanjing, JS, China
AbeBooks Seller since April 7, 2009
Condition: New
Quantity: 10 available
Add to basketShip out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Pages Number: 189 Publisher: Science Press Pub. Date :2007-6-1. The book includes: artificial neural networks. neural processes. the process of feed-forward neural network. the process of neural network learning algorithm. the feedback process neural networks. multi-aggregation process neural networks. Contents: Chapter 1 Introduction 1.1 1.2 development of artificial intelligence artificial intelligence systems are characterized by fuzzy calculation 1.3 Computational Intelligence 1.3.1 1.3.2 1.3.4 Evolutionary Computation Neural Computation 1.3.3 three branches of the process of neural networks with 1.4 Chapter 2 artificial neural network biological neurons 2.1 2.2 2.3 Mathematical model of neuron feedforward feedback neural network 2.3.1 feedforward feedback neural network model 2.3.2 feedforward neural network function approximation ability before 2.3.3 feed-forward neural network computing 2.3.4 feedforward neural network learning algorithm 2.3.5 feedforward neural network generalization problems 2.3.6 feedforward neural network applications' 2.4 2.4.1 fuzzy fuzzy neural network neurons 2.4.2 2.5 nonlinear fuzzy neural network aggregation fraction of artificial neural networks 2.5.1 2.5.2 Aggregation great artificial neural network (or small) polymer artificial neural networks 2.5.3 Other non-linear 2.6 Aggregation of artificial neural network with temporal and spatial aggregation process neural network artificial neural network 2.7 Chapter 3 of the classification process of biological neurons neurons Inspiration 3.1 3.2 3.3 process definition process neurons and neurons during neural fuzzy functional 3.4 the process of neurons per 3.4.1 3.4.2 fuzzy fuzzy weighted by the fuzzy inference rules constructed during the process of neurons 3.5 complex function of neurons and feed-forward process in Chapter 4 4.1 feed forward neural network neural network during a simple model 4.2 feed forward neural network process model 4.3 based on the general right to start the process of function-based neural network model 4.4 feed forward neural network processes the basic theorem of the existence of solutions 4.4.2 4.4.1 4.4.3 Pan-continuity function approximation properties 4.4.4 4.5 fraction of computing power feed-forward neural network during the process of neuronal 4.5.2 4.5.1 fractional fractional process neural network model 4.6 are time-varying input and output functions of process neural network 4.6 4.6.2 .1 network continuity and approximation capability of the model 4.7 continuous process of continuous neural networks 4.7.1 4.7.2 continuous process neural network model 4.7.3 process neural model of continuity. capacity and computing power close to 4.8 functional neural networks 4.8.1 4.8.2 Functional feedforward neural functional neural network model 4.9 Conclusion Chapter 5. the process of neural network learning algorithm 5.1 Newton's method based on gradient descent and decrease the learning algorithm based on 5.1.1 The general gradient descent learning algorithm 5.1.2 Newton's method combined with a gradient-based learning algorithm based on Newton downhill method 5.1.3 The learning algorithm . . Chapter 6. the feedback process neural network aggregation process more than Chapter 7 Chapter 8 neural networks process neural network design and build Chapter 9 process neural network applications ReferencesFour Satisfaction guaranteed,or money back.
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