نتایج جستجو برای: adaptive neural fuzzy intelligent system
تعداد نتایج: 2675404 فیلتر نتایج به سال:
Chaos is a nonlinear behavior that shows chaotic and irregular responses to internal and external stimuli in dynamic systems. This behavior usually appears in systems that are highly sensitive to initial condition. In these systems, stabilization is a highly considerable tool for eliminating aberrant behaviors. In this paper, the problem of stabilization and tracking the chaos are investigated....
In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...
in this paper, the enhancement of vehicle stability and handling is investigated by control of the active geometry suspension system (ags). this system could be changed through control of suspension mounting point’s position in the perpendicular direction to wishbone therefore the dynamic is alternative and characteristics need to change. for this purpose, suitable controller needs to change mo...
This paper deals problem of intelligent hybrid systems. Intelligent systems include neural networks (NN), fuzzy systems (FS) and genetic algorithms (GA). Each of these intelligent systems has certain properties (ability of learning, modelling, classifying, obtaining empirical rules, solving optimizing tasks ...) fitting specific kind of applications. Combination of these intelligent systems cre...
We propose Adaptive Fuzzy Neural Trees as an appropriate tool for intelligent data analysis, comprehension , and prediction. Instead of using a single technique Adaptive Fuzzy Neural Trees as a mixture of paradigms combine the main advantages of neural networks, decision trees, and fuzzy logic. Like neural networks they are able to model smooth functions and can be adapted incrementally. Like d...
The paper introduces evolving connectionist systems (ECOS) as an effective approach to building on-line, adaptive intelligent systems. ECOS evolve through incremental, hybrid (supervised/unsupervised), on-line learning. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of ...
The paper introduces evolving connectionist systems (ECOS) as an effective approach to building on-line, adaptive intelligent systems. ECOS evolve through incremental, hybrid (supervised/unsupervised), on-line learning. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of ...
The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, online learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created ...
nowadays, it has demonstrated that viruses can be transmitted by water and foods. therefore, it causes the research to develop for detecting different viruses in water and foods. among foods, milk can transfer potentially pathogenic viruses. on the other hand, to achieve every method for recovery and extraction of viruses in raw milk it needs to know about impact of milk components on viruses. ...
The application of Artificial Intelligent approaches was introduced recently in protection of distribution networks. These approaches started with introducing Fuzzy Inference System (FIS), then using Artificial Neural Network (ANN).In this research, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) for protection of bus bars will be illustrated. The ANFIS can be viewed as a fuzzy...
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