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Computational Intelligence in Time Series Forecasting Theory and Engineering Applications

:	Ajoy K. Palit, Dobrivoje Popovic
:	Computational Intelligence in Time Series Forecasting Theory and Engineering Applications
:	Springer-Verlag
:	9781846281846
:	1
:	CHF 121.10
:

:	Anwendungs-Software
:	English

:	372
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:	PC/MAC/eReader/Tablet
:	PDF

Foresight in an engineering business can make the difference between success and failure, and can be vital to the effective control of industrial systems. The authors of this book harness the power of intelligent technologies individually and in combination.

6 Neuro-fuzzy Approach (p.223)

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 asfuzzy-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.

	Series Editors’ Foreword	9
	Preface	11
	Contents	15
	Part I Introduction	22
	1 Computational Intelligence: An Introduction	24
	1.1 Introduction	24
	1.2 Soft Computing	24
	1.3 Probabilistic Reasoning	25
	1.4 Evolutionary Computation	27
	1.5 Computational Intelligence	29
	1.6 Hybrid Computational Technology	30
	1.7 Application Areas	31
	1.8 Applications in Industry	32
	References	33
	2 Traditional Problem Definition	38
	2.1 Introduction to Time Series Analysis	38
	2.2 Traditional Problem Definition	39
	2.2.1 Characteristic Features	39
	2.2.1.1 Stationarity	39
	2.2.1.2 Linearity	41
	2.2.1.3 Trend	41
	2.2.1.4 Seasonality	42
	2.2.1.5 Estimation and Elimination of Trend and Seasonality	42
	2.3 Classification of Time Series	43
	2.3.1 Linear Time Series	44
	2.3.2 Nonlinear Time Series	44
	2.3.3 Univariate Time Series	44
	2.3.4 Multivariate Time Series	45
	2.3.5 Chaotic Time Series	45
	2.4 Time Series Analysis	46
	2.4.1 Objectives of Analysis	46
	2.4.2 Time Series Modelling	47
	2.4.3 Time Series Models	47
	2.5 Regressive Models	48
	2.5.1 Autoregression Model	48
	2.5.2 Moving-average Model	49
	2.5.3 ARMA Model	49
	2.5.4 ARIMA Model	50
	2.5.5 CARMAX Model	53
	2.5.6 Multivariate Time Series Models	54
	2.5.7 Linear Time Series Models	56
	2.5.8 Nonlinear Time Series Models	56
	2.5.9 Chaotic Time Series Models	57
	2.6 Time-domain Models	58
	2.6.1 Transfer-function Models	58
	2.6.2 State-space Models	59
	2.7 Frequency-domain Models	60
	2.8 Model Building	63
	2.8.1 Model Identification	64
	2.8.2 Model Estimation	66
	2.8.3 Model Validation and Diagnostic Check	69
	2.9 Forecasting Methods	70
	2.9.1 Some Forecasting Issues	71
	2.9.2 Forecasting Using Trend Analysis	72
	2.9.3 Forecasting Using Regression Approaches	72
	2.9.4 Forecasting Using the Box-Jenkins Method	74
	2.9.5 For