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Learning systems

Author: Eduard Avedʼyan; J Mason; P C Parks
Publisher: London ; New York : Springer, ©1995.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
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A learning system can be defined as a system which can adapt its behaviour to become more effective at a particular task. It consists of an architecture with a set of variable parameters and an  Read more...

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Additional Physical Format: Online version:
Avedʼyan, Eduard, 1937-
Learning systems.
London ; New York : Springer, ©1995
(OCoLC)605304627
Online version:
Avedʼyan, Eduard, 1937-
Learning systems.
London ; New York : Springer, ©1995
(OCoLC)607895744
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Eduard Avedʼyan; J Mason; P C Parks
ISBN: 3540199969 9783540199960
OCLC Number: 33044431
Description: xiii, 119 pages : illustrations ; 24 cm
Contents: 1 Introduction to Learning Systems.- 1.1 Systems, Memory.- 1.2 Performance Index.- 1.2.1 Random Input.- 1.2.2 Deterministic Input.- 1.3 Learning Algorithms.- 1.4 Some Examples of Learning Systems.- 1.4.1 The Learning Linear Combiner.- 1.4.2 Neurons of Higher Order.- 1.4.3 The Learning Non-Linear Transformer.- 1.4.4 Linear and Non-Linear Learning Filters.- 1.4.5 Learning control systems.- 1.4.6 The Learning Multilayer Neural Network.- 1.4.7 Learning CMAC.- References.- 2 Deterministic Algorithms.- 2.1 Simple Projection Algorithms in Spaces With Different Norms (Structure, Convergence, Properties).- 2.1.1 Construction Of Kaczmarz Algorithm.- 2.1.2 Convergence.- 2.2 Modified Projection Algorithms With a High Rate of Convergence.- 2.2.1 Construction.- 2.2.2 Transient Mode.- 2.2.3 Properties Of The Estimates.- 2.2.4 "Bad" Measurements.- References.- 3 Deterministic and Stochastic Algorithms of Optimisation.- 3.1 Deterministic Methods for Unconstrained Minimisation.- 3.1.1 The Gradient Algorithm.- 3.1.2 Convergence.- 3.1.3 Newton's Algorithm.- 3.1.4 Example Application.- 3.2 Stochastic Approximation and Recurrent Estimation.- 3.2.1 Random Input.- 3.2.2 Example System.- References.- 4 Stochastic Algorithms: The Least Squares Method in the Non-Recurrent and Recurrent Forms and the Gauss-Markov Theorem.- 4.1 The Least Squares Method in Recursive and Non-Recursive Forms (The White Noise Case).- 4.1.1 The Least Squares Method in Non-Recursive Form.- 4.1.2 A Priori Information.- 4.2 The Gauss-Markov Theorem.- 4.2.1 Optimal Estimates.- 4.2.2 Connection With the Maximum Likelihood Estimates [2].- 4.3 Example System.- References.- 5 Stochastic Algorithms.- 5.1 Algorithms With Forgetting Factor.- 5.2 The Least Squares Method by Correlated Noise in the Non-Recursive and Recursive Forms: Connection With Decorrelation Procedures.- 5.2.1 The Arbitrary Case.- 5.2.2 Special Case.- 5.3 Introduction to the Kaiman filter [1], [3].- References.- 6 Multilayer Neural Networks.- 6.1 Multilayer Neural Network as a Non-Linear Transformer. The Kolmogorov and Cybenko Theorems.- 6.1.1 Introduction.- 6.1.2 MNN Architecture.- 6.1.3 The Input-Output Mapping of a Multilayer Network of the First Order.- 6.1.4 Approximation.- 6.2 Learning Algorithms for Single Elements of Multilayer Neural Networks.- 6.2.1 Differentiable Activation Functions.- 6.2.2 Non-Differentiable Activation Functions.- References.- 7 Learning Algorithms for Neural Networks.- 7.1 The Back-Propagation Algorithm for MNN Learning.- 7.1.1 Main Equations.- 7.1.2 Fully Connected MNNs.- 7.1.3 The Transient Sensitivity Matrix D(i+1) for Fully Connected MNNs of the First Order.- 7.1.4 The Transient Sensitivity Matrix D(i+1) for Fully Connected MNNs of the Second Order.- 7.1.5 Non-Fully Connected MNNs.- 7.2 Autonomous Algorithms for Adjusting MNNs.- 7.2.1 Introduction.- 7.2.2 Neural Networks With a Single Output.- 7.2.3 Neural Network With Many Outputs.- References.- 8 Identification and Control of Dynamic Systems Using Multilayer Neural Networks.- 8.1 Identification of Dynamic Systems Using Multilayer Neural Networks.- 8.1.1. Linear Systems.- 8.1.2 Non-Linear Systems.- 8.1.3 The Structure of MNNs for Identification.- 8.2 Control of Dynamic Systems Using Multilayer Neural Networks.- 8.2.1 Linear Systems.- 8.3 Control of Non-Linear Dynamic Systems Using Multilayer Neural Networks.- 8.3.1 Example use of Neural Networks for MRAC.- References.- 9 The Cerebellar Model Articulation Controller (CMAC).- 9.1 Introduction to CMAC.- 9.1.1 Introduction.- 9.2 Data Storage and Learning Process in the CMAC.- 9.2.1 Introduction to the CMAC.- 9.2.2 The CMAC system.- 9.3 Albus' Learning Algorithm.- 9.4 Modified Albus' Algorithm.- 9.4.1 Modification.- 9.4.2 Special Features of the Modified Albus Algorithm.- 9.5 CMAC for Identification and Adaptive Control.- References.
Responsibility: Eduard Avedʼyan ; edited by J. Mason and P.C. Parks.

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