Rahib Hidayat Abiyev

Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study

One of the main problems for effective control of an uncertain system is the creation of the proper knowledge base for the control system. In this paper, the integration of fuzzy set theory and wavelet neural networks (WNNs) is proposed to alleviate the problem. The proposed fuzzy WNN is constructed on the base of a set of fuzzy rules. Each rule includes a wavelet function in the consequent part of the rule. The parameter update rules of the system are derived based on the gradient descent method. The structure is tested for the identification and the control of the dynamic plants commonly used in the literature. It is seen that the proposed structure results in a better performance despite its smaller parameter space.

Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants

In industry, most dynamical plants are characterized by unpredictable and hard-to-formulate factors, uncertainty, and fuzziness of information, and as a result, deterministic models usually prove to be insufficient to adequately describe the process. In such situations, the use of fuzzy approaches becomes a viable alternative. However, the systems constructed on the base of type 1 fuzzy systems cannot directly handle the uncertainties associated with information or data in the knowledge base of the process. One possible way to alleviate the problem is to resort to the use of type 2 fuzzy systems. In this paper, the structure of a type 2 Takagi–Sugeno–Kang fuzzy neural system is presented, and its parameter update rule is derived based on fuzzy clustering and gradient learning algorithm. Its performance for identification and control of time-varying as well as some time-invariant plants is evaluated and compared with other approaches seen in the literature. It is seen that the proposed structure is a potential candidate for identification and control purposes of uncertain plants, with the uncertainties being handled adequately by type 2 fuzzy sets.

A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization

The integration of fuzzy systems and neural networks has recently become a popular approach in engineering fields for modelling and control of uncertain systems. This paper presents the development of novel type-2 neuro-fuzzy system for identification of time-varying systems and equalization of time-varying channels using clustering and gradient algorithms. It combines the advantages of type-2 fuzzy systems and neural networks. The type-2 fuzzy system allows handling the uncertainties associated with information or data in the knowledge base of the process. The structure of the proposed type-2 TSK fuzzy neural system (FNS) is given and its parameter update rule is derived, based on fuzzy clustering and gradient learning algorithm. The proposed structure is used for identification and noise equalization of time-varying systems. The effectiveness of the proposed system is evaluated by comparing the results obtained by the use of models seen in the literature.

Navigation of mobile robots in the presence of obstacles

Robot navigation is one of the basic problems in robotics. In general, the robot navigation algorithms are classified as global or local, depending on surrounding environment. In global navigation, the environment surrounding the robot is known and the path which avoids the obstacle is selected. In local navigation, the environment surrounding the robot is unknown, and sensors are used to detect the obstacles and avoid collision. In the past, a number of algorithms have been designed by many researchers for robot navigation problems. This paper presents software simulation of navigation problems of a mobile robot avoiding obstacles in a static environment using both classical and fuzzy based algorithms. The simulation environment is a menu-driven one where one can draw obstacles of standard shapes and sizes and assign the starting and ending points of the mobile robot. The robot will then navigate among these obstacles without hitting them and reach the specified goal point.

A servo system control with time-varying and nonlinear load conditions using type-2 TSK fuzzy neural system

Abstract: A type-2 Takagi-Sugeno-Kang fuzzy neural system is proposed and its parameter update rules are derived using fuzzy clustering and gradient learning algorithms. The proposed type-2 fuzzy neural system is used for the control and the identification of a real-time servo system. Fuzzy c-means clustering algorithm is used to determine the initial places of the membership functions to ensure that the gradient descent algorithm used afterwards converges in a shorter time. A number of different load conditions including nonlinear and time-varying ones are used to investigate the performance of the proposed control algorithm. The control structure has the ability to regulate the servo system with reduced oscillations when compared with the results of its type-1 counterpart around the set point signal in the presence of load disturbances.

A type-2 fuzzy wavelet neural network for system identification and control

Abstract This paper proposes a novel, type-2 fuzzy wavelet neural network (type-2 FWNN) structure that combines the advantages of type-2 fuzzy systems and wavelet neural networks for identification and control of nonlinear uncertain systems. The proposed network is constructed on the base of a set of fuzzy rules that includes type-2 fuzzy sets in the antecedent part and wavelet functions in the consequent part. For structure identification, a fuzzy clustering algorithm is implemented to generate the rules automatically and for parameter identification the gradient learning algorithm is used. The effectiveness of the proposed system is evaluated for identification and control problems of time-invariant and time-varying systems. The results obtained are compared with those obtained by the use of type-1 FWNN based systems and other similar studies.

Fuzzy portfolio selection using genetic algorithm

This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.

A type-2 fuzzy wavelet neural network for time series prediction

This paper presents the development of novel type-2 wavelet neural network system for time series prediction. The structure of type-2 Fuzzy Wavelet Neural Network (FWNN) is proposed and its learning algorithm is derived. The proposed network is constructed on the base of a set of fuzzy rules that includes type-2 fuzzy sets in the antecedent part and a wavelet function in the consequent part of the rules. For generating the structure of prediction model a fuzzy clustering algorithm is implemented to generate the rules automatically and the gradient learning algorithm is used for parameter identification. Type-2 FWNN is used for modelling and prediction of exchange rate time series. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of type-2 FWNN based systems and with the comparative simulation results of previous related models.

Design of an adaptive interval type-2 fuzzy logic controller for the position control of a servo system with an intelligent sensor

Type-2 fuzzy logic systems are proposed as an alternative solution in the literature when a system has a large amount of uncertainties and type-1 fuzzy systems come to the limits of their performances. In this study, an adaptive type-2 fuzzy-neuro system is designed for the position control of a servo system with an intelligent sensor. The sensor gives different resistance values with respect to the stretch of it, and it is supposed to be used in an robotic arm position measurement system. These kinds of sensors can be used in human-assistance robots that have soft surfaces in order not to damage the humans. However, these sensors have time-varying gains and uncertainties that are not very easy to handle. Moreover, they generally have a hysteresis on their input-output relations. The simulation results show that the control algorithm developed gives better performances when compared to conventional type-1 fuzzy controllers on such a highly nonlinear, uncertain system.

Identification and Control of Dynamic Plants Using Fuzzy Wavelet Neural Networks

This paper presents a fuzzy wavelet neural network (FWNN) for identification and control of a dynamic plant. The FWNN is constructed on the basis of fuzzy rules that incorporate wavelet functions in their consequent parts. The architecture of the control system is presented and the parameter update rules of the system are derived. Learning rules are based on the gradient decent method and genetic algorithm (GA). The structure is tested for the identification and the control of the dynamic plants commonly used in the literature. It is shown that the proposed structure results in a better performance despite its smaller parameter space.