The paper presents the neuro-fuzzy controller algorithm for air conditioning system. Neuro-fuzzy control combines the learning capabilities of neural networks and control capabilities of fuzzy logic control. The neurofuzzy controller for air conditioning system takes two inputs from temperature and humidity sensors and controls the compressor speed. The experimental results of the developed sys...

An evolving cascade neuro-fuzzy system and its online learning procedure are proposed in this paper. The system is based on nodes of a special type. A quality estimation process is defined by finding an optimal value of the used cluster validity index. Keywords— Evolving cascade system, neuro-fuzzy network, data stream, fuzzy clustering.

We address the prediction of the gene structure using a new method and tools, involving the sequence of distances between bases and neuro-fuzzy predictors. The method is tested on the HIV virus genome and the results look promising compared to other methods. We suggest that new, global prediction methods based on implicit, not explicit knowledge, may be as strong as the current, largely explici...

Abstract: In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central-limit theorem, is also shown. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence betw...

The goal of this work is to compare fuzzy, neural network and neuro-fuzzy approaches to the control of mobile robots. The rst part of this paper is devoted to the formal framework of fuzzy controllers. Results of an example of their use for a mobile robot are discussed. As an experimental platform, the Khepera mobile robot is used. The same example is studied using artiicial neural networks. Fo...

1 This paper describes DANIELA, a Neuro-Fuzzy system for control applications. The system is based on a custom neural device that can implement either Multi-Layer Perceptrons, Radial Basis Functions or Fuzzy paradigms. The system implements intelligent control algorithms mixing neuro-fuzzy algorithms with nite state automata and is used to control a walking hexapod.

An approach in modeling collaborative and metacognitive data is presented in this paper. The proposed scheme, namely Collaboration/ Metacognition–Adaptive Network-based Fuzzy Inference System (C/M-ANFIS), uses neurofuzzy structure to adaptively infer on the relation between the above data in a meaningful way. More specifically, the collaborative and metacognitive data refer to the participant’s...

A fuzzy controller is suited to control Antilock Brake System (ABS) however time complexity of fuzzy controller is high order. Problem Solution Data Structure (PSDS) has been introduced as a data-oriented model of fuzzy controller to reduce the response time. Locally learning of PSDS controller has been left as an open issue. Fuzzy Learning Mechanism (FLM) has been introduced by using fuzzy inf...

An innovative adaptive control method for speed control of induction motor based on field oriented control is presented in this paper. The fusion of sliding-mode and type-2 neuro fuzzy systems is used to control this system. An online learning algorithm based on sliding-mode training algorithm, and type-2 fuzzy systems is employed to deal with parametric uncertainties and disturbances, by adjus...

Classical control theory is based on the mathematical models that describe the physical plant under consideration. The essence of fuzzy control is to build a model of human expert who is capable of controlling the plant without thinking in terms of mathematical model. The transformation of expert's knowledge in terms of control rules to fuzzy frame work has not been formalized and arbitrary cho...