Design a Technology Based on the Fusion of Genetic Algorithm, Neural network and Fuzzy logic (original) (raw)
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International Journal of Computer Science and Technology , 2015
Neural Network (NN) and Genetic Algorithm (GA) are two very known methodology for optimizing and learning. Each having its own strengths and weakness. These two have generally evolved along separate paths. Recently there have been attempts to combine the two technologies. This research is devoted to implement a method for combining genetic algorithm with neural network (GANN). A collaborative approach has been used in this research. To integrated GA and NN into a single system, a population of neural networks is evolved, i.e., the goal of the proposed system is to find the optimal neural network solution. In collaborative system GA and NN work parallel; for optimization it is very necessary to work with parallel.Several MATLAB functions and tools have been used to implement the proposed GANN method. The development and experiments demonstration of GANN is done on MATLAB 7.0.12. The proposed method consists of neural learning by the backpropagation algorithm and applies evolutionary, genetic operators. The learning behavior of the algorithm was tested on a simple pattern recognition example and it was able to prove its performance. The new ideas, concepts and processes of GANN bring new life in the field of Artificial Intelligenceresearch.
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The natural development o f h ybrid techniques causes biases with their roots in di erent technologies, in this case either in fuzzy systems or in neural networks. The neuro-fuzzy research is discussed in this paper giving examples and emphasising the neural network perspective. Introduction of new fuzzy systems models and the development of new neural learning algorithms could be observed in the development of neuro-fuzzy research. The self-evolving character of those new neural algorithms capable of building the architecture of neuro-fuzzy systems from data, proves to be an useful tool for data analysis and knowledge fusion applications.
NEURO-FUZZY SYSTEMS: A HYBRID INTELLIGENT APPROACH
Integration of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN and FIS are obvious. There are several approaches to integrate ANN and FIS and very often it depends on the application. Neuro-fuzzy modelling can be regarded as a gray-box technique on the boundary between neural networks and qualitative fuzzy models. The tools for building neuro-fuzzy models are based on combinations of algorithms from the fields of neural networks, pattern recognition and regression analysis. In this paper, an overview of neuro-fuzzy modelling given.
An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic
Handbook of Research on Artificial Intelligence Techniques and Algorithms
Intelligent System (IS) can be defined as the system that incorporates intelligence into applications being handled by machines. The chapter extensively discusses the role of Genetic Algorithm (GA) in the search and optimization process along with discussing applications developed so far. A very detailed discussion on the Fuzzy Rule-Based System is presented along with major applications developed in different domains. The chapter presents algorithm of implementing intelligent procedure to decide whether a patient is prone to heart disease or not. The procedure evolves solutions using genetic operators and provides its decision automatically. The chapter presents discussion on the results achieved as a result of prototypical implementation of the evolutionary fuzzy hybrid model. The significant advantage of the presented research work is that applications that do not have any mathematical formulation and still demand optimization can be easily solved using the designed approach.
The need to solve highly nonlinear, time variant problems has been growing rapidly as many of today’s applications have nonlinear and uncertain behaviour which changes with time. Currently, no model based method exists that can effectively these problems in a general way. These problems, coupled with others (such as problems in decision making, prediction, etc.) have inspired a growing interest in intelligent techniques including Fuzzy Logic, Neural Networks, Genetic Algorithms, Expert Systems, and Probabilistic Reasoning. Intelligent Systems, in general, use various combinations of these techniques to address real world complex problems. In this paper, I have addressed the intelligent systems based on various combinations of neural nets and fuzzy logic, called Neural Fuzzy Systems (NFS). The rationale to combine fuzzy logic with neural nets is emphasized to alleviate the limitations of each of these technologies while adding their advantages.
The techniques of artificial intelligence based in fuzzy logic and neural networks are frequently applied together. The reasons to combine these two paradigms come out of the difficulties and inherent limitations of each isolated paradigm. Generically, when they are used in a combined way, they are called Neuro-Fuzzy Systems. This term, however, is often used to assign a specific type of system that integrates both techniques. This type of system is characterised by a fuzzy system where fuzzy sets and fuzzy rules are adjusted using input output patterns. There are several different implementations of neuro-fuzzy systems, where each author defined its own model. This article summarizes a general vision of the area describing the most known hybrid neuro-fuzzy techniques, its advantages and disadvantages.
Neuro-fuzzy fusion: a new approach to multiple classifier system
… , 2006. ICIT'06. 9th …, 2006
To meet this we propose a neuro-fuzzy fusion (NFF) method for fusing the responses of a set of fuzzy classifiers. In the proposed method the output of the considered classifiers are fed to a neural network which performs the fusion task. Five labeled data sets, of which two are from remote sensing images, have been used for the performance comparison of various MCSs. Experimental study revealed the improved classification capability of the proposed NFF based MCS yielding consistently better results for all data sets.