Using fuzzy logic to integrate neural networks and knowledge-based systems

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems

Expert systems and finite element structural analysis - a review

Finite element analysis of many engineering systems is practised more as an art than as a science . It involves high level expertise (analytical as well as heuristic) regarding problem modelling (e .g. problem specification,13; choosing the appropriate type of elements etc .), optical mesh design for achieving the specified accuracy (e .g . initial mesh selection, adaptive mesh refinement), selection of the appropriate type of analysis and solution13; routines and, finally, diagnosis of the finite element solutions . Very often such expertise is highly dispersed and is not available at a single place with a single expert. The design of an expert system, such that the necessary expertise is available to a novice to perform the same job even in the absence of trained experts, becomes an attractive proposition. 13; In this paper, the areas of finite element structural analysis which require experience and decision-making capabilities are explored . A simple expert system, with a feasible knowledge base for problem modelling, optimal mesh design, type of analysis and solution routines, and diagnosis, is outlined. Several efforts in these directions, reported in the open literature, are also reviewed in this paper

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

A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Intelligent adaptive testing system

Modern learning is impossible without automated knowledge testing systems. At present, the most progressive are adaptive testing models in which the complexity of tasks varies depending on the correctness of the patient’s answers. This article describes the development of an intelligent adaptive testing system using a fuzzy mathematics device. An intelligent adaptive testing system has been developed; the module that implements the expert system uses the production base of the rules. The input parameters of testing are the percentage of correct responses, the degree of correctness of the response, the duration of the response, and the number of attempts. The output is a change in the current level of training of the student on the basis of which test questions of related complexity are selected. As a method of logical inference, the Mamdani method is used which consists of six operational actions: phazification — conversion of exact values of input variables into values of linguistic variables through belonging functions, this served as the basis for designing a fuzzy base of rules of the expert system; aggregation of sub-conditions — determination of the truth of conditions for each linguistic rule of the fuzzy inference system; activating sub-conclusions — finding the degree of truth of each of the sub-conclusions in the linguistic rule; accumulation of conclusions — finding the belonging function for each of the output linguistic variables; defazzification — finding a numerical value for each of the output linguistic variables. A developed intelligent adaptive testing system (ISAT) is presented that allows, based on the analysis of test results, to determine the current level of training of students, to adapt the material to the level of their training. This system allows you to dynamically present questions of appropriate complexity in real time. When using the developed intelligent adaptive testing system, students will be provided with questions of the appropriate level of complexity, this will allow building an individual learning trajectory. The introduction of a predefined system will ensure the implementation of a personalized approach for organizing the learning process; will increase the accuracy of assessing students’ knowledge. The use of the technology of fuzzy expert systems allows for automated, intelligent control of students’ knowledge

A comparison of fuzzy approaches to e-commerce review rating prediction

This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fuzzy ARTMAP (SFAM) was compared on six datasets containing customer reviews. The results revealed that all computational intelligence predictors were suitable for the rating prediction problem, and that the genetic algorithm is effective in reducing the number of dimensions without affecting the prediction performance of each computational intelligence predictor