1 Feedforward Neural Networks: An Introduction (Simon Haykin).
1.1 Supervised Learning.
1.2 Unsupervised Learning.
1.3 Temporal Processing Using Feedforward Networks.
1.4 Concluding Remarks.
2 Uniform Approximation and Nonlinear Network Structures (Irwin W. Sandberg).
2.2 General Structures for Classification.
2.3 Myopic Maps, Neural Network Approximations, and Volterra Series.
2.4 Separation Conditions and Approximation of Discrete-Time and Discrete-Space Systems.
2.5 Concluding Comments.
3 Robust Neural Networks (James T. Lo).
3.3 General Risk-Sensitive Functionals.
3.4 Approximation of Functions by MLPs.
3.5 Approximation of Functions by RBFs.
3.6 Formulation of Risk-Sensitive Identification of Systems.
3.7 Series-Parallel Identification by Artificial Neural Networks (ANNs).
3.8 Paral lel Identification of ANNs.
4 Modeling, Segmentation, and Classification of Nonlinear Nonstationary Time Series (Craig L. Fancourt and Jose C. Principe).
4.2 Supervised Sequential Change Detection.
4.3 Unsupervised Sequential Segmentation.
4.4 Memoryless Mixture Models.
4.5 Mixture Models for Processes with Memory.
4.6 Gated Competitive Experts.
4.7 Competitive Temporal Principal Component Analysis.
4.8 Output-Based Gating Algorithms.
4.9 Other Approaches.
5 Application of Feedforward Networks to Speech (Shigeru Katagiri).
5.2 Fundamentals of Speech Signals and Processing Technologies.
5.3 Fundamental Issues of ANN Design.
5.4 Speech Recognition.
5.5 Applications to Other Types of Speech Processing.
5.6 Concluding Remarks.