Fuzzy neural expert system with automated extraction of fuzzy If-Then rules from a trained neural network

The author previously (1990, 1991) proposed a fuzzy neural expert system and provided a method to extract automatically fuzzy IF-THEN rules from a trained neural network. The previous work is extended and a neural expert system is proposed using fuzzy teaching input. The neural expert system can perform generalization of the information derived from training data with fuzzy teaching input and embodiment of knowledge in the form of a fuzzy neural network, where the fuzzy teaching input is subjectively given by domain experts: and extraction of fuzzy IF-THEN rules with linguistic relative importance of each proposition in an antecedent (IF-part) from a trained neural network. A method is proposed to extract automatically fuzzy IF-THEN rules from the trained neural network generated by training data with fuzzy teaching input. >

We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network. Knowledge is acquired from domain experts as fuzzy rules and membership functions. Then, they are converted into a neural network which implements fuzzy inference without rule matching. The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy. The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts. Also, by modifying the weights of neural networks adaptively, the problem of belief propagation in conventional expert systems can be solved easily. Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.

A new method of ozone forecasting using fuzzy expert and neural network systems

In this chapter, the integration of neural networks and fuzzy systems will be discussed. A substantial portion of this material comes from reference [1]. The combination of the techniques of fuzzy logic systems and neural networks suggests the novel idea of transforming the burden of designing fuzzy logic control and decision systems to the training and learning of connectionist neural networks. This neuro-fuzzy and/or fuzzy-neural synergistic integration reaps the benefits of both neural networks and fuzzy logic systems. That is, the neural networks provide connectionist structure (fault tolerance and distributed representation properties) and learning abilities to the fuzzy logic systems, and the fuzzy logic systems provide the neural networks with a structural framework with high-level fuzzy IF-THEN rule thinking and reasoning. These benefits can be witnessed in three major integrated systems: neural fuzzy systems, fuzzy neural networks, and fuzzy neural hybrid systems. These three integrated systems, along with their applications, will be discussed and explored in the next six chapters, as well as in the Appendices.

Credit risk is the main risk that Chinese commercial banks are facing. Taking into account three categories of risk factors, namely risk factors of enterprise, risk factors of commercial bank and risk factors of macroscopic economy, an index system was set up. Then, according to the index system and the characteristics of fuzzy neural network and expert system, a credit risk rating system based on fuzzy neural network and expert system was proposed.

The nursing care quality improvement is very important for our life. Currently, nursing-care freestyle texts (nursing-care data) are collected from many hospitals in Japan by using Web applications. The collected nursing-care data are stored into the database. To evaluate nursing-care data, we have already proposed a fuzzy classification system, a neural network based system, a support vector machine (SVM) based classification system. Then, in order to improve the classification performance, we have proposed a genetic algorithm (GA) based feature selection method for generating numerical data from collected nursing-care texts.In this paper, we propose a fuzzy rule extraction method from the nursing-care text data. First, features of nursing-care texts are selected by a genetic algorithm based feature selection method. Next, numerical training data are generated by using selected features. Then we train neural networks using generated training data. Finally, fuzzy if-then rules are extracted from the trained neural networks by the parallelized rule extraction method.From computer simulation results, we show the effectiveness of our proposed method.

Since it is hard to get training set of fuzzy neural network, to understand knowledge rules, and to learn new knowledge through fuzzy expert system, an attribute recognition system based on rough set theory-fuzzy neural network and fuzzy expert system has been put forward. In this paper, it has explained how to use rough set theory to get training set of fuzzy neural network, how to deal with data through fuzzy neural network and fuzzy expert system parallelly, and how to acquire new knowledge from fuzzy neural network to supplement the knowledge database of fuzzy expert system. It has fully utilized the capability of rough set theory that is to simplify large amount of redundant data, the capabilities of fuzzy neural network that are self-learning, fault-tolerant and highly nonlinear mapping, and the capability of fuzzy expert system that is reasoning quality in knowledge. Experiments show the exactness and high-efficient quality of this recognition system, and it has gotten more than 96% correct recognition rate.

This paper studies traffic variable estimation, and presents a method of estimation for the number of vehicle waiting for queue (NVWQ) based on neuro-fuzzy at urban intersection. We present results of training the neural network for a detectorized intersection in Changsha City. The accuracy of NVWQ estimation using the fuzzy neural networks approaches is more than 90%. The fuzzy neural networks have the advantages of both fuzzy expert systems (knowledge representation) and artificial neural networks (learning). The fuzzy neural networks can be trained successfully to estimate NVWQ for different traffic flow patterns and different conditions of intersection. This greatly reduces a lot of effort of extracting a traffic expert's knowledge into fuzzy if-then rules. All we have to do is to present the training data to the network which can figure out its own rules through internal representation. In the traffic signal control system, detection of traffic variables at intersection, such as NVWQ is very important and is the basic input data to determine signal timing. We also discuss traffic signal control based on fuzzy system and genetic algorithms (GA). The fuzzy controller has the ability to adjust its signal timing in response to the changing traffic conditions on a real-time basis. Our proposed controller produces lower vehicle delays and percentage of stopped vehicles than the traffic-actuated controller.

Neural fuzzy networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. Examples are the Falcon-ART, and the POPFNN family of networks. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. This correspondence proposes two new networks: Falcon-FKP and Falcon-PFKP. They are extensions of the Falcon-ART network, and aimed to overcome the shortcomings faced by the Falcon-ART network itself, i.e., poor classification ability when the classes of input data are very similar to each other, termination of training cycle depends heavily on a preset error parameter, the fuzzy rule base of the Falcon-ART network may not be consistent Nauck, there is no control over the number of fuzzy rules generated, and learning efficiency may deteriorate by using complementarily coded training data. These deficiencies are essentially inherent to the fuzzy ART, clustering technique employed by the Falcon-ART network. Hence, two clustering techniques--Fuzzy Kohonen Partitioning (FKP) and its pseudo variant PFKP, are synthesized with the basic Falcon structure to compute the fuzzy sets and to automatically derive the fuzzy rules from the training data. The resultant neural fuzzy networks are Falcon-FKP and Falcon-PFKP, respectively. These two proposed networks have a lean and efficient training algorithm and consistent fuzzy rule bases. Extensive simulations are conducted using the two networks and their performances are encouraging when benchmarked against other neural and neural fuzzy systems.

This paper combines neural network with expert system organically,adopts fuzzy membership function to calculate fuzzy relationship matrix to reduce the neural network algorithm's dependence on training data, and on this basis, a newfashioned fuzzy neural expert system of fan is established. The system uses modular design and provides examples of diagnosis. The results show that The system performances high reasoning efficiency, good reliability, and satisfies the effectiveness and practicality of fault diagnosis of fan as well. The study can be extended to fault diagnosis of other rotating machinery.

Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This special issue entitled"Rule Extraction from Data" is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the genetic-fuzzy system genetic-equation system and the neural network system were used in determining the feeding rate of the coagulant. Fuzzy system and neural network system are excellently robust in multivariables and nonlinear problems. but fuzzy system is difficult to construct the fuzzy parameter such as the rule table and the membership function. Therefore we made the genetic-fuzzy system by the fusion of genetic algorithms and fuzzy system, and also made the feeding rate equation by genetic algorithms. To train fuzzy system, equation parameter and neural network system, the actual operation data of the water treatment plant was used. We determined optimized feeding rates of coagulant by the fuzzy system, the equation and the neural network and also compared them with the feeding rates of the actual operation data.

This paper focuses on one half of the knowledge acquisition problem for fuzzy systems, namely the acquisition of a fuzzy rule base from a set of input/output data, and in particular on the extraction of a set of fuzzy rules from a trained neural network. Some limitations with previously reported work in this area are first identified. Two simple rule extraction techniques are then described and tested on a well known classification problem. The performance of the resultant rule bases compares more favourably than those reported using alternative techniques.

This paper presents a description of an algorithm that is used for tuning fuzzy certainty factors in a fuzzy expert system. Many expert system building tools have been developed, but only a few can produce systems that deal with inexact knowledge. One of the models that can deal with inexact knowledge is the fuzzy certainty factor expert system. Given a fuzzy certainty factor expert system, one of the very difficult tasks in its development is the tuning of the fuzzy certainty factors. This paper presents a method for using backpropagation, a well-known neural network training algorithm, for tuning of certainty factors. The fuzzy certainty factor expert system is defined, and then it is tuned by translating or mapping the fuzzy logic system to a feedforward neural network framework. The tuning then takes place as a session analogous to neural network training. This method is shown to be much more efficient than previous tuning methods. The application area of dental diagnostics is used to demonstrate the method. The system is reviewed and results that show its efficacy are discussed.

This paper studies traffic variable estimation, and presents a method of estimation for the number of vehicle waiting for queue (NVWQ) based on neuro-fuzzy at urban intersection. We present results of training the neural network for a detectorized intersection in Changsha City. The accuracy of NVQW estimation using the fuzzy neural networks approaches is more than 90%. The fuzzy neural networks have advantages of both fuzzy expert systems (knowledge representation) and artificial neural networks (learning). The fuzzy neural networks can be trained successfully to estimate NVWQ for different traffic flow patterns and different conditions intersection. This greatly reduces a lot of effort of extracting traffic expert's knowledge into fuzzy if-then rules. All we have to do is to present training data to the network which will figure out its own rules through internal representation. In traffic signal control system, detection of traffic variables at intersection, such as NVWQ is very important and is the basic input data to determine signal timing.