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Adaptive fuzzy neural network systems (AFNNS) are employed for adaptive noise cancellation using multi-sensory signal recording of the same noise source. The method based on AFNNS not only achieves the optimal reconstruction but also possesses a desired robust against the effect of uncertainties and incomplete information in signal processing. Simulation result shows that the design method can not...
In this paper, an approach of learning the values of the weights in weighted fuzzy if-then rules is presented. Based on the concept of T-S norms, firstly, this paper presents the T-S norm-based fuzzy reasoning algorithm; secondly, we map a set of initial fuzzy if-then rules, in which all the weights are equal to 1.0, and the T-S norm-based fuzzy reasoning methodology into a forward fuzzy neural network,...
In manufacturing processes it is very important that the condition of the cutting tool, particularly the indications when it should be changed, can be monitored. Cutting tool condition monitoring is a very complex process and thus sensor fusion techniques and artificial intelligence signal processing algorithms are employed in this study. The multi-sensor signals reflect the tool condition comprehensively...
The fuzzy hit-or-miss transformation is a fuzzy morphological operator, which is a key for the feature extraction in the ambiguous information. In this paper, a neural network implementation for fuzzy morphological operators is proposed, and by means of a training method and differentiable equivalent representations for the operators, efficient adaptive algorithms to optimize structuring elements...
For nonlinear friction force in hydraulic position tracking system, partition compensation is employed to solve the problem of large approximation error caused by non-smooth characteristic of friction when the friction is compensated globally by fuzzy neural network (FNN). The experimental results show that the partition compensation algorithm is effective in compensating the nonlinearity, and the...
In this paper, the mathematical models and high effective combination numerical methods for calculating of stationary and dynamic temperature field of a profile part of a blade with convective cooling are put forward. The theoretical substantiation of these methods is proved by appropriate theorems. For it, converging integral processes have been developed and the estimations of errors in the terms...
In this paper, a systematic guideline is introduced to design a stable adaptive fuzzy wavelet controller with sliding mode for a class of uncertain nonlinear systems. Based on the Lyapunov synthesis approach, we construct the fuzzy wavelet controller such that it can basically control and guarantee the stability of the whole control system. On the other hand, a robust controller is designed to restrain...
The contamination condition of insulators is usually estimated by detecting the root mean square (r.m.s) of surface leakage current via online monitoring system, ignoring the influence of environment factors, such as temperature, humidity, etc. For the detection factors have fuzzy characters, a new method based on fuzzy neural network is proposed in order to overcome the disadvantages of traditional...
In this paper, the exponential stability problems for fuzzy cellular neural networks with time-varying delays are investigated. We first give a condition of the existence and uniqueness of the equilibrium point for systems under consideration. Then, using the methods of varying parametric and inequality analysis technique, we obtained exponential stability criteria and estimation of the convergence...
Power requirement is one of the most important process parameters in cylindrical traverse grinding. Due to the inherent complexity of the process, it may be difficult to derive the exact mathematical expression of the input-output variables relationships. An expert system is developed in this paper, based on the fuzzy basis function network (FBFN) to predict power requirement in grinding process....
This paper presents the design, development of dynamic load simulator based on dynamic fuzzy neural networks (D-FNNs) controller. Dynamic load simulator (DLS) can reproduce desired load torque acting on loaded object to test its performance and stability. In DLS, the redundancy torque caused by the motion of loaded object has a very poor effect on the loading accuracy. So a simplified dynamic model...
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