RECURRENT NEURAL NETWORKS AND SOFT COMPUTING pot

RECURRENT NEURAL NETWORKS AND SOFT COMPUTING Edited by Mahmoud ElHefnawi and Mohamed Mysara Recurrent Neural Networks and Soft Computing Edited by Mahmoud ElHefnawi and Mohamed Mysara Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Sasa Leporic Technical Editor Teodora Smiljanic Cover Designer InTech Design Team First published March, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Recurrent Neural Networks and Soft Computing, Edited by Mahmoud ElHefnawi and Mohamed Mysara p. cm. ISBN 978-953-51-0409-4 Contents Preface IX Part 1 Soft Computing 1 Chapter 1 Neural Networks and Static Modelling 3 Igor Belič Chapter 2 A Framework for Bridging the Gap Between Symbolic and Non-Symbolic AI 23 Gehan Abouelseoud and Amin Shoukry Chapter 3 Ranking Indices for Fuzzy Numbers 49 Tayebeh Hajjari Chapter 4 Neuro-Fuzzy Digital Filter 73 José de Jesús Medel, Juan Carlos García and Juan Carlos Sánchez Part 2 Recurrent Neural Network 87 Chapter 5 Recurrent Neural Network with Human Simulator Based Virtual Reality 89 Yousif I. Al Mashhadany Chapter 6 Recurrent Neural Network-Based Adaptive Controller Design for Nonlinear Dynamical Systems 115 Hong Wei Ge and Guo Zhen Tan Chapter 7 BRNN-SVM: Increasing the Strength of Domain Signal to Improve Protein Domain Prediction Accuracy 133 Kalsum U. Hassan, Razib M. Othman, Rohayanti Hassan, Hishammuddin Asmuni, Jumail Taliba and Shahreen Kasim Chapter 8 Recurrent Self-Organizing Map for Severe Weather Patterns Recognition 151 José Alberto Sá, Brígida Rocha, Arthur Almeida and José Ricardo Souza VI Contents Chapter 9 Centralized Distributed Parameter Bioprocess Identification and I-Term Control Using Recurrent Neural Network Model 175 Ieroham Baruch, Eloy Echeverria-Saldierna and Rosalba Galvan-Guerra Chapter 10 Optimization of Mapping Graphs of Parallel Programs onto Graphs of Distributed Computer Systems by Recurrent Neural Network 203 Mikhail S. Tarkov Chapter 11 Detection and Classification of Adult and Fetal ECG Using Recurrent Neural Networks, Embedded Volterra and Higher-Order Statistics 225 Walid A. Zgallai Chapter 12 Artificial Intelligence Techniques Applied to Electromagnetic Interference Problems Between Power Lines and Metal Pipelines 253 Dan D. Micu, Georgios C. Christoforidis and Levente Czumbil Chapter 13 An Application of Jordan Pi-Sigma Neural Network for the Prediction of Temperature Time Series Signal 275 Rozaida Ghazali, Noor Aida Husaini, Lokman Hakim Ismail and Noor Azah Samsuddin Preface Section 1: Soft Computing This section illustrates some general concepts of artificial neural networks, their properties, mode of training, static training (feedforward) and dynamic training (recurrent), training data classification, supervised, semi-supervised and unsupervised training. Prof. Belic Igor’s chapter that deals with ANN application in modeling, illustrating two properties of ANN: universality and optimization. Prof. Shoukry Amin discusses both symbolic and non-symbolic data and ways of bridging neural networks with fuzzy logic, as discussed in the second chapter including an application to the robot problem. Dr. Hajjari Tayebeh discusses fuzzy logic and various ordering indices approaches in detail, including defuzzification method, reference set method and the fuzzy relation method. A comparative example is provided indicating the superiority of each approach. The next chapter discusses the combination of ANN and fuzzy logic, where neural weights are adjusted dynamically considering the fuzzy logic adaptable properties. Dr. Medel Jeus describes applications of artificial neural networks, both feed forward and recurrent, and manners of improving the algorithms by combining fuzzy logic and digital filters. Finally Dr. Ghaemi O. Kambiz discusses using genetic algorithm enhanced fuzzy logic to handle two medical related problems: Hemodynamic control & regulation of blood glucose. Section 2: Recurrent Neural Network Recurrent Neural Networks (RNNs), are like other ANN abstractions of biological nervous systems, yet they differ from them in allowing using their internal memory of the training to be fed recurrently to the neural network. This makes them applicable for adaptive robotics, speech recognition, attentive vision, music composition, hand-writing recognition, etc. There are several types of RNNs, such as Fully recurrent network, Hopfield network, Elman networks and Jordan networks, Echo state network, Long short term memory network, Bi-directional RNN, Continuous-time RNN, Hierarchical RNN, Recurrent multilayer perceptron, etc. In this section, some of these types of RNN are discussed, as well as application of each type. Dr. Al-Mashhadany Yousif Illustrates types of ANN providing a detailed illustration of RNN, types and advantages with application to human simulator. This chapter discusses time-delay recurrent neural network (TDRNN) model, as a novel approach. Prof. Ge Hongwei’s chapter X Preface demonstrates the advantages of using such an approach over other approaches using a graphic illustration of the results. Moreover, it illustrates its usage in non-linear systems. Dr Othman Razib M. describes in his chapter the usage of a combination of Bidirectional Recurrent Neural Network and Support vector machine (BRNN-SVM) with the aim of protein domain prediction, where BRNN acts to predict the secondary structure as SVM later processes the produced data to classify the domain. The chapter is well constructed and easy to understand. Dr. Rocha Brigida’s chapter discusses using Recurrent self-organizing map (RSOM) for weather prediction. It applies Self-Organizing Map (SOM), single layer neural network. To deal with the dynamic nature of the data, Temporal Kohonen Map (TKM) was implemented including the temporal dimension, which is followed by applying Recurrent Self-Organizing Map (RSOM) to open the window for model retraining. It proceeds to describe data preprocessing, training and evaluation in a comparative analysis of these three approaches. Dr. Baruch Ieroham describes the usage of different RNN approaches in aerobic digestion bioprocess featuring dynamic Backpropagation and the Levenberg-Marquardt algorithms. It provides sufficient introductory information, mathematical background, training methodology, models built, and results analysis. Dr. Tarkov Mikhail discusses using RNN for solving the problem of mapping graphs of parallel programs, mapping graph onto graph of a distributed CS as one of the approaches of over heading and optimization solutions. They used different mapping approaches, as Hopfield, achieving improvement in mapping quality with less frequency, Splitting that achieved higher frequency, Wang was found to achieve higher frequency of optimal solutions. Then they proposed using nesting ring structures in ”toroidal graphs with edge defect” and “splitting method”. Dr. Zgallai Walid A discusses the use of RNN in ECG classification and detection targeting different medical cases, adult and fetal ECG as well as other ECG abnormalities. In Prof. Micu Dan Doru’s work, Electromagnetic Interference problems are described and how they could be solved using artificial intelligence. The authors apply various artificial intelligence techniques featuring two models, one for evaluation of magnetic vector potential, and the other for self & mutual impedance matrix determination. Finally Dr. Ghazali Rozaida discusses the use of Jordan Pi-Sigma Neural Network (that featuring RNN) for weather forecasting, providing sufficient background information, training and testing against other methods, MLP and PSNN. We hope this book will be of value and interest to researchers, students and those working in the artificial intelligence, machine learning, and related fields. It offers a balanced combination of theory and application, and each algorithm/method is applied to a different problem and evaluated statistically using robust measures like ROC and other parameters. Mahmoud ElHefnawi Division of Genetic Engineering and Biotechnology, National Research Centre & Biotechnology, Faculty of Science, American University in Cairo, Egypt Mohamed Mysara (MSc). Biomedical Informatics and Chemoinformatics Group, National Research Centre, Cairo, Egypt . RECURRENT NEURAL NETWORKS AND SOFT COMPUTING Edited by Mahmoud ElHefnawi and Mohamed Mysara Recurrent Neural Networks and Soft Computing Edited. orders@intechopen.com Recurrent Neural Networks and Soft Computing, Edited by Mahmoud ElHefnawi and Mohamed Mysara p. cm. ISBN 978-953-51-0409-4 Contents Preface IX Part 1 Soft Computing. Computer Systems by Recurrent Neural Network 203 Mikhail S. Tarkov Chapter 11 Detection and Classification of Adult and Fetal ECG Using Recurrent Neural Networks, Embedded Volterra and Higher-Order