Computational Intelligence in Time Series Forecasting
Description
Foresight in an engineering enterprise can make the difference between success and failure, and can be vital to the effective control of industrial systems. Applying time series analysis in the on-line milieu of most industrial plants has been problematic owing to the time and computational effort required. The advent of soft computing tools offers a solution.
The authors harness the power of intelligent technologies individually and in combination. Examples of the particular systems and processes susceptible to each technique are investigated, cultivating a comprehensive exposition of the improvements on offer in quality, model building and predictive control and the selection of appropriate tools from the plethora available.
Application-oriented engineers in process control, manufacturing, production industry and research centres will find much to interest them in this book. It is suitable for industrial training purposes, as well as serving as valuable reference material for experimental researchers.
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From the reviews:
This is a monograph whose aim is of special and singular interest: to present systematic and comprehensive methods and techniques of computational intelligence and soft computing for solving forecasting and prediction problems . of time series. The book is designed to be largely self-contained and is devoted to offer researchers, practicing engineers, and applications-oriented professionals a reference volume and a valuable guide for the design, building and execution of forecasting and prediction experiments . . The entire monograph is sensibly structured . .
Zentralblatt MATH 1095 (2006) (Reviewer: Neculai Curteanu)
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6.1 Motivation for Technology Merging
Contemporary intelligent technologies have various characteristic features that can be used to implement systems that mimic the behaviour of human beings. For example, expert systems are capable of reasoning about the facts and situations using the rules out of a specific domain, etc. The outstanding feature of neural networks is their capability of learning, which can help in building artificial systems for pattern recognition, classification, etc. Fuzzy logic systems, again, are capable of interpreting the imprecise data that can be helpful in making possible decisions. On the other hand, genetic algorithms provide implementation of random, parallel solution search procedures within a large search space.
Therefore, in fact, the complementary features of individual categories of intelligent technologies make them ideal for isolated use in solving some specific problems, but not well suited for solving other kinds of intelligent problem. For example, the black-box modelling approach through neural networks is evidently well suited for process modelling or for intelligent control, but less suitable for decision making. On the other hand, the fuzzy logic systems can easily handle imprecise data, and explain their decisions in the context of the available facts in linguistic form; however, they cannot automatically acquire the linguistic rules to make those decisions. Such capabilities and restrictions of individual intelligent technologies have actually been a central driving force behind their fusion for creation of hybrid intelligent systems capable of solving many complex problems.
The permanent growing interest in intelligent technology merging, particularly in merging of neural and fuzzy technology, the two technologies that complement each other (Bezdek, 1993), to create neuro-fuzzy or fuzzy-neural structures, has largely extended the capabilities of both technologies in hybrid intelligent systems. The advantages of neural networks in learning and adaptation and those of fuzzy logic systems in dealing with the issues of human-like reasoning on a linguistic level, transparency and interpretability of the generated model, and handling of uncertain or imprecise data, enable building of higher level intelligent systems. The synergism of integrating neural networks with fuzzy logic technology into a hybrid functional system with low-level learning and high-level reasoning transforms the burden of the tedious design problems of the fuzzy logic decision systems to the learning of connectionist neural networks. In this way the approximation capability and the overall performance of the resulting system are enhanced.
A number of different schemes and architectures of this hybrid system have been proposed, such as fuzzy-logic-based neurons (Pedrycz, 1995), fuzzy neurons (Gupta, 1994), neural networks with fuzzy weights (Buckley and Hayashi, 1994), neuro-fuzzy adaptive models (Brown and Harris, 1994), etc. The proposed architectures have been successful in solving various engineering and real-world problems, such as in applications like system identification and modelling, process control, systems diagnosis, cognitive simulation, classification, pattern recognition, image processing, engineering design, financial trading, signal processing, time series prediction and forecasting, etc.
