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Foundation and Application of Expert System Verification and Validation
Published in Jay Liebowitz, The Handbook of Applied Expert Systems, 2019
Hybrid intelligent systems are systems that combine the advantages offered by expert systems, neural networks, and fuzzy systems. The need for such systems has emerged due to the fact that we are at a stage of development in which further increase in performance of existing tools requires the use of intelligent tools offered by these systems. Until now, their use was limited to stand-alone architectures; today the intelligent hybrid systems are the emerging technology that could take advantage of the best of each technology’s features. This synergistic approach recognizes both similarities and differences between these systems, and suggests that for many particular applications the resultant hybrid system could reflect the best aspects of each component of the system (Vermesan and Vermesan, 1996).
Hybrid Systems
Published in Paresh Chra Deka, A Primer on Machine Learning Applications in Civil Engineering, 2019
A hybrid intelligent system is one that combines at least two intelligent technologies. For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy system. The combination of probabilistic reasoning, fuzzy logic, neural networks, and evolutionary computation forms the core of soft computing, an emerging approach to building hybrid intelligent systems capable of reasoning and learning in an uncertain and imprecise environment.
Adaptive Neuro-Fuzzy Inference System-Based Bass Gura Controller for Solar-Powered SEPIC Converter
Published in IETE Journal of Research, 2022
B. Lekshmi Sree, M. G. Umamaheswari, A. Sangari, C. Komathi, S. Durgadevi, Gajendran Marimuthu
In the resultant hybrid intelligent system, the neural network has the ability to recognize patterns and adapt itself to cope up with changing environment. On the other hand, the fuzzy inference system incorporates human knowledge and performs inference and decision making. The selection of appropriate membership functions is shown in Figure 4(a). Figure 4(b) shows the surface viewer for the ANFIS-based BG controller. Figures 5–7 illustrate the structure of ANFIS for the proposed system. To select a proper rule base using the back-propagation algorithm, the neural network techniques are implemented. Each membership function (MF) is denoted by a pre-defined shape and its equivalent parameters.
Artificial intelligence as an upcoming technology in wastewater treatment: a comprehensive review
Published in Environmental Technology Reviews, 2021
Hybrid intelligent systems integrate various artificial intelligence methods by using models which complement each other and improve the intelligence of the system more effectively. Hybrid intelligent systems combine two or more AI technologies to overcome the major disadvantage in a single AI method, and develop collaborative ability to perform better. The synergies of different techniques in one computational model makes these systems possess an extended range of capabilities. These systems are capable of reasoning and learning in uncertain, imprecise, and dynamic environments. Individual AI models have some drawbacks which inhibit them from achieving desired performance in complicated problems.