Computational Intelligence
Description
Computational Intelligence is tolerant of imprecise information, partial truth and uncertainty. This book presents a selected collection of contributions on a focused treatment of important elements of CI, centred on its key element: learning. This book presents novel applications and real world applications working in Manufacturing and Engineering, and it sets a basis for understanding Domotic and Production Methods of the XXI Century.
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Content
Dedication. Contributing Authors. Preface. Acknowledgments. Chapter 1: Soft Computing and its Applications in Engineering and Manufacture; DT Pham, PTN Pham, MS Packianather, AA Afify. Chapter 2: Neural Networks Historical Review; D Andina, A Vega-Corona, JI Seijas, J Torres-Garcia.. Chapter 3: Artificial Neural Networks; DT Pham, MS Packianather, AA Fify. Chapter 4: Application of Neural Networks; D Andina, A Vega-Corona, JI Seijas, JM Alarcon. Chapter 5: Radial Basis Function Networks and their Application on Communication Systems; A Gallardo-Antolin, J Pascual-Garcia, JL Sancho-Gomez. Chapter 6: Biological Clues for Up-to-date Artificial Neuron; J Ropero-Pelaez, JR Castillo-Piqueira. Chapter 7: Support Vector Machines; J Gomez-Saenz-de-Tjada, J Seijas. Chapter 8: Fractals as Preprocessing Tool for Computational Intelligence Applications; A Tarquis, V Mendez, JM Anton, JB Grau, D Andina.
CHAPTER 2 NEURAL NETWORKS HISTORICAL REVIEW (p. 39)
D. ANDINA, A. VEGA-CORONA, J. I. SEIJAS, J. TORRES-GARCÍA
Abstract:
This chapter starts with a historical summary of the evolution of Neural Networks from the first models which are very limited in application capabilities to the present ones that make possible to think in applying automatic process to tasks that formerly had been reserved to the human intelligence. After the historical review, Neural Networks are dealt from a computational point of view. This perspective helps to compare Neural Systems with classical Computing Systems and leads to a formal and common presentation that will be used throughout the book
INTRODUCTION
Computers used nowadays can make a great variety of tasks (whenever they are well defined) at a higher speed and with more reliability than those reached by the human beings. None of us will be, for example, able to solve complex mathematical equations at the speed that a personal computer will. Nevertheless, mental capacity of the human beings is still higher than the one of machines in a wide variety of tasks.
No artificial system of image recognition is able to compete with the capacity of a human being to discern between objects of diverse forms and directions, in fact it would not even be able to compete with the capacity of an insect. In the same way, whereas a computer performs an enormous amount of computation and restrictive conditions to recognize, for example, phonemes, an adult human recognizes without no effort words pronounced by different people, at different speeds, accents and intonations, even in the presence of environmental noise.
It is observed that, by means of rules learned from the experience, the human being is much more effective than the computers in the resolution of imprecise problems (ambiguous problems), or problems that require great amount of information. Our brain reaches these objectives by means of thousands of millions of simple cells, called neurons, which are interconnected to each other.
However, it is estimated that the operational amplifiers and logical gates can make operations several orders of magnitude faster than the neurons. If the same processing technique of biological elements were implemented with operational amplifiers and logical gates, one could construct machines relatively cheap and able to process as much information, at least, as the one that processes a biological brain. Of course, we are too far from knowing if these machines will be constructed one day.
Therefore, there are strong reasons to think about the viability to tackle certain problems by means of parallel systems that process information and learn by means of principles taken from the brain systems of living beings. Such systems are called Artificial Neural Networks, connexionist models or distributed parallel process models. Artificial Neural Networks (ANNs or, simply, NNs) come then from the man’s intention of simulating the biological brain system in an artificial way.
1. HISTORICAL PERSPECTIVE
The science of Artificial Neural Networks did his first significant appearance during the 1940’s. Researchers who tried to emulate the functions of the human brain developed physical models (later, simulations by means of programs) of the biological neurons and their interconnections.
