Digital image classification by optimised fuzzy system

This paper developed a fuzzy logic controller with a genetic algorithm control system for sensor based pipeline robot navigation in pipe environment. The fuzzy controller creates initial membership functions and fuzzy logic rules. the genetic algorithm, which automatically designs a fuzzy logic controller by modifying the position of controller membership functions and the commands given to the pipeline robot, optimizes the fuzzy controller. This GA-fuzzy system is used to control the mobile pipeline robot, which is equipped with an array of ultrasonic sensors to acquire the distance between the pipeline robot and obstacle. On the basis of this system, a strategy for combining local behavior control with global behavior control planning was proposed in this paper. Finally a pipeline robot runs in simulation environment. The experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved well enough to smoothly drive the pipeline robot in different pipe environment.

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm

Fuzzy control strategy is developed for the dual-clutch single-axis torque coupling parallel hybrid electric vehicle. In this paper the torque distribution fuzzy controller which has been designed for the hybrid vehicle which is optimized by genetic algorithms. The simulation model of the hybrid vehicle was built upon matlab / simulink and ADVISOR software. Then a fuzzy rules and correspondent membership functions had been established and the input language variable and output language variable use trapeziform and deltoid membership functions. After design of fuzzy logic torque controller, the genetic algorithm was introduced and used it to optimize the fuzzy logic torque controller. Under typical condition NEDC, the fuzzy control strategy is optimized both by genetic algorithms with the constraint condition of economy performance and by integrated constraint conditions of economy performance and emission performance. Optimization results show that when the controller is only optimize fuzzy control strategy for economy performance the fuel consumption decreased by 5.3% but the emission of CO and NOx both increased, but when the controller both optimize fuzzy control strategy for economy performance and emission performance the fuel consumption decreased by 4.3% with emission quality improved. So the fuzzy control strategy optimize by the genetic algorithm can improve the fuel consumption obvious.

The nuclear steam generator is a nonminimum-phase system, which is caused by the swell and shrink effects. Since its inverse system has unstable dynamics, it is difficult to train the fuzzy controller via the conventional backpropagation of the system output errors. In this paper, a genetic algorithm is applied for the simultaneous design of membership functions and rule sets for a fuzzy control method for a steam generator water level. The genetic fuzzy controller for the steam generator is a fuzzy logic controller which is tuned offline by the genetic algorithm using the water level, feedwater flowrate, and steam flowrate signals of the steam generator. The symmetric Gaussian membership functions based on the flowrate and water level errors are applied. The proposed genetic fuzzy controller has a generalized and simplified rule base. The same genetic algorithm that is used to optimize the genetic fuzzy controller tunes a conventional proportional-integral (P-I) controller, and the performance of two controllers is compared. The genetic fuzzy controller shows good response that its swell and shrink phenomena are smaller and its response is faster than those of a well-tuned P-I controller are.

This paper examines the applicability of genetic algorithms (GA's) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology. When GA's have been used to develop both, it has been done serially, e.g., design the membership functions and then use them in the design of the rule set. This, however, means that the membership functions were optimized for the initial rule set and not the rule set designed subsequently. GA's are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. This new method has been applied to two problems, a cart controller and a truck controller. Beyond the development of these controllers, we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, we study the application of fuzzy set theory, genetic algorithms and rough set theory techniques to the control of the rectifier. A fuzzy adaptive control scheme with the aid of rough set theory via genetic algorithms (GAs) finding the center parameters in place of the classical control is proposed. On the one hand, genetic algorithm gets optimal parameters of an accurate domain model, which tunes the scaling factors of fuzzy adaptive control but is hardly established in a non-liner system. On the other hand, fuzzy adaptive control deals with the dynamics and complexity of responses from rectifier of a HVDC system in the operation points, by adjusting its control parameters with the aid of rough tuner adaptively. Our study includes a brief introduction to fuzzy control and rough set algorithms, both theory and application. We also evaluate the performance of rough fuzzy control by simulation in the paper. The focus of our experiments is on the constant current control in rectifier side of HVDC system. The result shows there are many improvements offered by the fuzzy control scheme based on rough set theory in comparison with the only fuzzy control scheme in the rectifier side of HVDC system.

Watermarking techniques can be applied in many applications, such as copyright protection, authentication, fingerprinting, and data hiding. Each different purpose of usability requires different approaches and schemes. In this paper, we present the framework of combining the Advanced Encryption Standard (AES) with watermarking techniques for hiding secret information in digital images. This framework can also be customized for other type of multimedia objects.KeywordsExtended VersionWatermark ImageSecret MessageBlock CipherAdvance Encryption StandardThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An interval type-2 fuzzy logic system (IT2FLS) can function well with uncertain data, with which a type-1 fuzzy logic system (T1FLS) is ineffective because its membership function rests upon crisp values. However, similar to T1FLSs, there are challenges associated with IT2FLSs in selecting parameters, which can significantly affect the accuracy of the classification results with their relatively high sensitivity. This paper discusses and proposes a hybrid model based on IT2FLS and particle swarm optimization (PSO) for prediction problems. The main objective of this paper is to find the optimal solution for the unknown fuzzy systems using labelled data for training the fuzzy system. The PSO technique was used to find the optimal parameters of the Gaussian membership functions which utilized for IT2FLSs. The authors tested two data sets for each of the two prediction problems, namely: burnt forest area prediction and wine quality prediction. The predictive results were compared with other predictive methods including random forest (RF), support vector machines (SVM), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and IT2FLS with parameters generated by using the fuzzy c-means algorithm (IT2FLS-FCM). Experiment results showed that the proposed method could significantly improve accuracy compared to several other predictive techniques.

A hybrid model based on modular neural networks and fuzzy systems for classification of blood pressure and hypertension risk diagnosis

Image Steganography provides secure communication through hiding confidential information in digital images. To increase security, embedding capacity, and attack resilience, this study proposes a hybrid picture steganography architecture that blends chaotic systems with evolutionary algorithms. The method divides the picture into sub-bands using the Integer Wavelet Transform (IWT), enabling the best possible data embedding. Chaotic systems in logistic maps create pseudo-random sequences, which determine safe sites for message bit embedding, providing randomness as well as immunity against statistical attacks. A genetic algorithm further optimizes the embedding process by searching the search space to identify an optimal chromosome representation, minimizing image distortion while maximizing capacity. The genetic algorithm is put through selection, crossover, and mutation to improve the embedding strategy step by step. With a Peak Signal-Tonoise Ratios (PSNR) of 55.3392 dB, Mean Squared Error (MSE) of 0.3620, Structures Similarities Index (SSIM) of 0.9744, and Visual Information Fidelity (VIF) of 0.9269, experimental comparisons demonstrate the effectiveness of the suggested approach. In comparison to traditional steganography In techniques, the approach also obtains a substantial capacity of concealed information, with 3.67 bits per pixel (BPP). The outcomes point out that the implementation of chaotic sequences along with genetic optimization increases security and attack-resistant resilience without compromising image quality. The method presented offers an improved, effective, and highly secure digital communication steganographic solution.

Design of a fuzzy controller requires specification of both membership functions and decision rules. Specification of membership function for a fuzzy logic controller has been an important issue. The traditional way of selecting membership functions has been, in most cases, an adhoc procedure. In this paper, an optimization algorithm based on simulated annealing for designing fuzzy membership functions for fuzzy controllers is introduced. An optimization algorithm for designing the membership functions and fuzzy rule base for a fuzzy controller is presented. An optimal fuzzy controller using proposed technique for backing up a truck problem is designed and implemented, in which optimal fuzzy membership functions and fuzzy rules are designed. Also, a neural network controller for the same truck problem is designed to compare our results.

Genetic fuzzy system for damage detection in beams and helicopter rotor blades

Although fuzzy logic controllers and expert systems have been successfully applied in many complex industrial processes, they experience a deficiency in knowledge acquisition and rely to a great extent on empirical and heuristic knowledge which, in many cases, cannot be objectively elicited. Among the problems to be resolved in fuzzy controller design are the determination of the linguistic state space, definition of the membership functions of each linguistic term and the derivation of the control rules. Some of these problems can be solved by application of machine learning. First, it is desirable to simplify and automate the specification of linguistic rules. Secondly, it is also desirable that modification of control rules is possible in order to cope with previously unknown or changes in process dynamics. Machine learning methods have, in recent years, emerged from the use of learning algorithms modelled on natural and biological systems. These methods attempt to abstract the advanced mechanisms of learning exhibited by such systems, which can consequently be applied to intelligent control. One of these new algorithms is the genetic algorithm which is modelled on the processes of natural evolution. The paper develops the application of genetic algorithm techniques for fuzzy controller design. Genetic algorithms are used to automate and introduce objective criteria in defining fuzzy controller parameters.

It is well known that the process of tuning a fuzzy logic controller is almost always a very complex task, which is time consuming, very laborious and often requires expert knowledge of the controlled system. Mapping of fuzzy logic controller's parameters (rule base and membership functions of input(s) and output(s)) into a performance measure in a closed analytical form is near impossible to get, and thus the use of any classical optimization method is automatically ruled out. Knowing this, genetic algorithms with a fitness function in a form of cumulative response error represent a good choice of the optimization method. This approach enables the use of offline optimization of membership functions' parameters (which are being coded into chromosomes). Sugeno-Takagi fuzzy logic controllers with a proportional and a derivative component, and also with a fixed rule base are used in this approach. Experimental results of both simulations and validations on real systems are given in this paper and they show the good performance of this approach.

This paper presents a fuzzy logic control strategy on four-wheel steering(4WS) vehicle based on a multi-body vehicle dynamic model. The multi-body vehicle dynamic model based on ADAMS can accurately predict the dynamics performance of the vehicle. Fuzzy logic is applied to track the yaw velocity of the two degrees of freedom ideal model through the co-simulation of ADAMS and Matlab Fuzzy control unit with the optimized membership function. The fuzzy control parameters are optimized and analyzed by a combined optimization algorithm (Genetic Algorithm (GA) and Nonlinear Programming Quadratic Line search (NLPQL) method) combined with response surface model (RSM). Single lane change test is chosen to validate the fuzzy control logic strategy. Simulation result shows that four-wheel steering vehicle with the fuzzy control logic strategy can improve vehicle handling stability greatly comparing with traditional front wheel steering.