Neurofuzzy and fuzzyneural techniques as tools of studying and analyzing complex problems are relatively new even though neural networks and fuzzy logic systems have been applied as computational intelligence structural e- ments for the last 40 years. Computational intelligence as an independent sci- tific field has grown over the years because of the development of these str- tural elements. Neural networks have been revived since 1982 after the seminal work of J. J. Hopfield and fuzzy sets have found a variety of applications since the pub- cation of the work of Lotfi Zadeh back in 1965. Artificial neural networks (ANN) have a large number of highly interconnected processing elements that usually operate in parallel and are configured in regular architectures. The c- lective behavior of an ANN, like a human brain, demonstrates the ability to learn,recall,and generalize from training patterns or data. The performance of neural networks depends on the computational function of the neurons in the network,the structure and topology of the network,and the learning rule or the update rule of the connecting weights. This concept of trainable neural n- works further strengthens the idea of utilizing the learning ability of neural networks to learn the fuzzy control rules,the membership functions and other parameters of a fuzzy logic control or decision systems,as we will explain later on,and this becomes the advantage of using a neural based fuzzy logic system in our analysis. On the other hand,fuzzy systems are structured numerical estimators.

1 Introduction.- 2 Integration of Neural and Fuzzy.- 2.1 Introduction.- 2.2 Hybrid Artificial Intelligent Systems.- References.- 3 Neuro-Fuzzy Applications in Speech Coding and Recognition.- 3.1 Introduction.- 3.2 Soft Computing.- 3.3 FuGeNeSys: A Neuro-Fuzzy Learning Tool for Fuzzy Modeling.- 3.4 Conventional Speech Coding and Recognition Techniques.- 3.5 A Soft Computing-Based Approach in Speech Classification.- 3.6 Neuro-Fuzzy Applications in Speech Coding and Recognition.- 3.7 Conclusions.- References.- 4 Image/Video Compression Using Neuro-Fuzzy Techniques.- 4.1 Introduction.- 4.2 Neuro-Fuzzy Techniques.- 4.3 Neuro-Fuzzy Based Vector Quantization for Image Compression.- 4.4 Image Transmission by NITF.- 4.5 Neuro-Fuzzy Based Video Compression.- References.- 5 A Neuro-Fuzzy System for Source Location and Tracking in Wireless Communications.- 5.1 Introduction.- 5.2 Problem Statement.- 5.3 The Architecture of the Fuzzy-Neural Network.- 5.4 Design of the Rule Base.- 5.5 Simulations.- 5.6 Neuro-Fuzzy System Evaluation.- References.- 6 Fuzzy-Neural Applications in Handoff.- 6.1 Introduction.- 6.2 Application of a Neuro-Fuzzy System to Handoffs in Cellular Communications.- References.- 6.3 Handoff Based Quality of Service Control in CDMA Systems Using Neuro-Fuzzy Techniques Bongkarn Homnan, Watit Benjapolakul.- References.- 7 An Application of Neuro-Fuzzy Systems for Access Control in Asynchronous Transfer Mode Networks.- 7.1 Introduction.- 7.2 Traffic Control in ATM Networks.- 7.3 Traffic Source Model and Traffic Policing Mechanism.- 7.4 Performance of FLLB Policing Mechanism.- 7.5 Performance of NFS LB Policing Mechanism.- 7.6 Evaluation of Simulation Results.- References.- Appendix A. Overview of Neural Networks.- A.1 Introduction.- A.2 Learning by Neural Networks.- A.3Examples of Neural Network Structures for PR Applications.- References.- Appendix B. Overview of Fuzzy Logic Systems.- B.1 Introduction.- B.2 Overview of Fuzzy Logic.- B.3 Examples.- References.- Appendix C. Examples of Fuzzy-Neural and Neuro-Fuzzy Integration.- C.1 Fuzzy-Neural Classification.- C.2 Fuzzy-Neural Clustering.- C.3 Fuzzy-Neural Models for Image Processing.- C.4 Fuzzy-Neural Networks for Speech Recognition.- C.5 Fuzzy-Neural Hybrid Systems for System Diagnosis.- C.6 Neuro-Fuzzy Adaptation of Learning Parameters - An Application in Chromatography.- References.