Screen LibraryNeural networks for intelligent signal processing /
This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their...
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| Main Authors: | |
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| Corporate Authors: | |
| Published: |
World Scientific Pub. Co.,
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| Publisher Address: | Singapore ; River Edge, N.J. : |
| Publication Dates: | 2003. |
| Literature type: | eBook |
| Language: | English |
| Series: |
Series on innovative intelligence ;
vol. 4 |
| Subjects: | |
| Online Access: |
http://www.worldscientific.com/worldscibooks/10.1142/5220#t=toc |
| Summary: |
This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN. |
| Carrier Form: | 1 online resource (xxiv,484pages) : illustrations. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9789812796851 (electronic bk.) |
| CLC: | TN911.7 |
| Contents: |
1. Introduction. 1.1. Motivation for ANNs. 1.2. ANN definitions and main types. 1.3. Specific ANN models. 1.4. ANN black box model. 1.5. ANN implementation. 1.6. When to use an ANN. 1.7. How to use an ANN. 1.8. General applications. 1.9. Pattern recognition examples. 1.10. Function mapping and filtering examples. 1.11. Motor control example. 1.12. ANN summary -- 2. A brief historical overview. 2.1. ANN history to 1970. 2.2. ANN history after 1970. 2.3. Reasons for the resurgence of interest in ANNs. 2.4. Historical summary -- 3. Basic concepts. 3.1. The basic model of the neuron. 3.2. Activation functions. 3.3. Topologies. 3.4. Learning. 3.5. The basic McCulloch Pitts and perceptron models. 3.6. Vectors spaces and matrix models. 3.7. Basic structure of a neural network. 3.8. Basic ANN operations in terms of matrices. 3.9. Why use matrices in ANNs? -- 4. ANN performance evaluation. 4.1. Confusion matrix. 4.2. Error measures including the square error. 4.3. Receiver-operating-characteristic (ROC). 4.4. Chi-squared goodness of fit -- 5. Basic pattern recognition principles. 5.1. Data pre-processing. 5.2. Feature measurement types. 5.3. Classification. 5.4. Decision criteria and output thresholds. 5.5. Design procedure for an ANN classifier. 5.6. Principle component analysis (PCA) -- 6. ADALINES, adaptive filters, and multi-layer perceptrons. 6.1. Adaptive linear combiner and AD ALINE. 6.2. General MLP networks -- 7. Probabilistic neural network classifier. 7.1. PNN theory. 7.2. Bayes' decision strategy. 7.3. PDF estimators and radial basis functions. 7.4. PNN architecture. 7.5. Features and application issues. 7.6. Applications of the PNN. 7.7. Gong classification application example. 7.8. Particle isolation application example. 7.9. FPGA PNN design -- 8. General regression neural network. 8.1. The Bayes theorem and regression theory. 8.2. The general regression neural network. 8.3. Short wave signal filtering application example -- 9.The modified probabilistic neural network. 9.1. MPNN theory. 9.2. Other MPNN characteristics. 9.3. MPNN and GRNN adaptation and learning scheme. 9.4. Signal processing application examples. 9.5. MPNN hardware implementation schemes. 9.6. MPNN summary. 10. Advanced MPNN developments. 10.1. A tuneable approximate piecewise linear model. 10.2. Integrated sensory intelligent system (ISIS). 10.3. Future directions for the MPNN -- 11. Neural networks similar to the common bandwidth spherical basis function regression ANNs. 11.1. Radial basis function neural network. 11.2. Cerebellar model articulation controller -- 12. Unsupervised learning neural networks. 12.1. Kohonen's self-organising map. 12.2. Adaptive resonance theory -- 13. Other neural network models. 13.1. Hopfield neural network. 13.2. Boltzmann machine. 13.3. Bidirectional associative memory. 13.4. Neocognitron -- 14. Statistical learning theory. 14.1. Learning and regularisation. 14.2. Vapnik's statistical learning theory. 14.3. Support vector learning machines -- 15. Application to intelligent signal processing. 15.1. Estimation or approximation theory. 15.2. General signal processing model. 15.3. Static ANN models. 15.4. Dynamic ANN models. 15.5. Application and signal Pre-processing issues. 15.6. Signal processing examples. 15.7. MPNN comparisons with other important ANNs -- 16. Application to intelligent control. 16.1. Utility of ANNs for control. 16.2. Generic approaches for controller design. 16.3. Neural control principles. 16.4. A fast adaptive neural network system -- 17. Discussion. 17.1. ANNs for intelligent engineering systems. 17.2. Signal processing. 17.3 Possible generic approaches. |