A neural network architecture for implementation of expert systems for real time monitoring

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered

Automated implementation of rule-based expert systems with neural networks for time-critical applications

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed

Approximation properties of the neuro-fuzzy minimum function

The integration of fuzzy logic systems and neural networks in data driven nonlinear modeling applications has generally been limited to functions based upon the multiplicative fuzzy implication rule for theoretical and computational reasons. We derive a universal approximation result for the minimum fuzzy implication rule as well as a differentiable substitute function that allows fast optimization and function approximation with neuro-fuzzy networks. --Fuzzy Logic,Neural Networks,Nonlinear Modeling,Optimization

Logic Negation with Spiking Neural P Systems

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing.Comment: 25 pages, 1 figur

Medical analysis and diagnosis by neural networks

In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neuro-fuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system. Keywords: Statistical Classification, Adaptive Prediction, Neural Networks, Neurofuzzy, Medical System

Distributed associative memories for high-speed symbolic reasoning

This paper briefly introduces a novel symbolic reasoning system based upon distributed associative memories which are constructed from correlation matrix memories (CMM). The system is aimed at high-speed rule-based symbolic operations. It has the advantage of very fast rule matching without the long training times normally associated with neural-network-based symbolic manipulation systems. In particular, the network is able to perform partial matching on symbolic information at high speed. As such, the system is aimed at the practical use of neural networks in high-speed reasoning systems. The paper describes the advantages and disadvantages of using CMM and shows how the approach overcomes those disadvantages. It then briefly describes a system incorporating CMM

A Neural-CBR System for Real Property Valuation

In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However, individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case- Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case- Based Reasoning systems

Improvement of fuzzy neural network using mine blast algorithm for classification of Malaysian Small Medium Enterprises based on strength

Fuzzy Neural Networks (FNNs) with the integration of fuzzy logic, neural networks and optimization techniques have not only solved the issue of “black box” in Artificial Neural Networks (ANNs) but also have been effective in a wide variety of real-world applications. Despite of attracting researchers in recent years and outperforming other fuzzy inference systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rule-base optimization methods to perform efficiently when the number of inputs increase. Many researchers have trained ANFIS parameters using metaheuristic algorithms but very few have considered optimizing the ANFIS rule-base. Mine Blast Algorithm (MBA) which has been improved by Improved MBA (IMBA) can be further improved by modifying its exploitation phase. This research proposes Accelerated MBA (AMBA) to accelerate convergence of IMBA. The AMBA is then employed in proposed effective technique for optimizing ANFIS rule-base. The ANFIS optimized by AMBA is used employed to model classification of Malaysian small medium enterprises (SMEs) based on strength using non-financial factors. The performance of the proposed classification model is validated on SME dataset obtained from SME Corporation Malaysia, and also on real-world benchmark classification problems like Breast Cancer, Iris, and Glass. The performance of the ANFIS optimization by AMBA is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), MBA and Improved MBA (IMBA), respectively. The results show that the proposed method achieved better accuracy with optimized rule-set in less number of iterations