Type-2 fuzzy systems

In Chap. 1, we begin by offering a brief introduction to the potential use of the chemical optimization method in different real-world applications. We describe the use of this method for optimizing type-2 fuzzy systems in problems of intelligent control of nonlinear plants. We also mention other possible applications of the proposed chemical optimization paradigm. 

Some types of knowledge-based systems may make judgments based on data that contain uncertainty we shall learn more of this in the next chapter when we encounter fuzzy systems. Even when the information that the system reasons with is unambiguous, the system s conclusions may come as a surprise to a nonexpert. If the user doubts whether the ES has reasoned correctly, it is natural for them to seek reassurance that the line of reasoning used is robust, so the ES must be able to do more than merely provide advice, it should be able to explain how it has reached a particular conclusion. 

Simulated annealing has been used in some fuzzy systems to learn or tune fuzzy systems (for example, see [8, 9, 22]). In addition, the combination of simulated annealing and type-1 Mamdani and TSK fuzzy systems exhibited good performance in forecasting Mackey-Glass time series as shown in [5] and [3]. In this... 

More specifically, a Mamdani-type Fuzzy Inference System, consisted of two inputs and one output, was developed. The system receives as inputs the displacement (m) and the velocity (u), while gives as output the increment of the control force (z). Triangular membership functions (trimf) were chosen both for inputs and output. These are shown in the following figures. 

Many types of combinations between fuzzy systems and neural networks have been proposed and studied. In what follows, we use the definitions and classification proposed by Detlef Nauck, from the Department of Computer Science, Technical University of Braunschweig, GermanyA neuro-fuzzy or neural fuzzy system is a combination of neural networks and fuzzy systems in such a way that neural networks are used to determine parameters of fuzzy systems. The main intention of a neuro-fuzzy approach is to create or improve a fuzzy system by means of neural network methods. The system should always be interpretable in terms of fuzzy if-then rules. A fuzzy... 

Spatially resolved material identification and classification is currently the prevalent application for SI systems. Of the many powerful spectral classifiers available, only two types, each with a number of different algorithms,14 could successfully be applied for real-time SI applications discriminant classifiers and dissimilarity-based classifiers. In addition, occasionally dedicated algorithms, such as fuzzy-classifiers, may be useful for special applications, for example, when there is no ab inito knowledge about the number and properties of the classification classes. 

In many of the early applications of fuzzy logic, the s and B s in the if-then rules had to be calibrated by cut-and-trial to achieve a desired level of performance. During the past few years, however, the techniques related to the induction of rules from observations have been developed to a point where the calibration of rules—by induction from input-output pairs—can be automated in a wide variety of cases. Particularly effective in this regard are techniques centered on the use of neural network methods and genetic computing for purposes of system identification and optimization. Many of the so-called neuro-fuzzy and fuzzy-genetic systems are of this type. 

Figure 25.2 Illustration of multiple-type cell co-culture systems (a) Random mixed, (b) contact patterning with fuzzy boundaries, (c) co-culture without cell contact on a flat surface, (d) in chambers, (e) the trans-well system, and (/) the micro cell culture analog ( xCCA) device.

Three approaches dominate the real-time intelligent control field (1) expert systems, (2) neural net controllers (neurocontrollers), and (3) fuzzy logic controllers [50]. These intelligent control systems are based on two types of information processing symbolic and subsymbolic processing [15,51]. 

Generally, in a terrestrial communication system, a transmitter possesses more processing ability than the receiver. Therefore, the major computational burden is easily taken up by the transmitter and less computational burden is left for the receiver. Since the proposed method is based on a preprocessing approach, it imparts more computational burden on the transmitting side than the receiving end and thus makes the receiver computationally less complex, fast and suitable for various real time applications. In addition, since this method is a spatial domain approach, it is computationally less complex than transform domain techniques such as DCT and wavelet. The proposed fuzzy-rule based method is a low complex, highly flexible and efficient technique that works fine with all types of video data. 

The presented method of subjective ship system risk estimation has been developed within a Ministry of Science and Higher Education research grant. Parts of the method dealing with estimation of the ship propulsion function loss were presented in more detail in publications (Brandowski et al. 2008 and Brandowski 2009). This paper deals mainly with estimation of the propulsion loss consequences. The estimation method is based exclusively on the judgements elicited by experts - experienced marine engineers. The method may be defined as fuzzy - numerical. The experts elicited their judgements partly in the numerical form (number of the ICF type events of propulsion loss in one year and the time at sea share in the ship active operation time) and partly in the linguistic form (the odds that specific consequences of the propulsion loss... 

Learning of Type-2 Fuzzy Logic Systems by Simulated Annealing with Adaptive Step Size... 

Type-1 fuzzy logic has been used successfully in a wide range of problems such as control system design, decision making, classification, system modelling and information retrieval [12, 31]. However, type-1 approach is not fully able to model uncertainties directly and minimise its effects [28]. These uncertainties exist in a large number of real-world applications. Uncertainties can be a result of [28] ... 

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