Pattern recognition using fuzzy logic and neural network techniques is usually related with image and speech processing. In a different approach, this paper shows results concerning the use of these techniques to extract models of electromechanical system’s. This approach is important because such systems always contain internal non-linear relations that are difficult to model and, in most cases, there are unknown internal phenomena dominating their dynamics. So, these techniques enable to recognise system patterns and to include them in a more complete system’s model. q 1997 Elsevier Science B.V
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...
Transforming noisy data into symbolic information is one of the crucial problems in syntactic patter...
The study objective was to construct models of multimass electromechanical systems using neural nets...
This paper describes the use of Fuzzy logic for the processing of EMG signals. This can increase the...
Audio and audio-pattern recognition is becoming one of the most important technologies to automatica...
[[abstract]]It has been known that fuzzy system provide a framework to handle uncertainties and vagu...
Abstract—Drive systems today determine the productivity and quality of industrial processes. However...
Pattern recognition systems play a role in applications as diverse as speech recognition, optical ch...
Some electromyogram (EMG) signals include information from limb functions and have been used to cont...
Biosignals processing, Biological Nonlinear and time-varying systems identification, Electomyograph ...
This book provides engineers and scientists in academia and industry with a thorough understanding o...
International audienceA technique is proposed that allows automatic decomposition of electromyograph...
This paper presents a novel learning algorithm for fuzzy logic neuron based on neural networks and f...
Neural networks and fuzzy techniques are among the most promising approaches to pattern recognition....
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...
Transforming noisy data into symbolic information is one of the crucial problems in syntactic patter...
The study objective was to construct models of multimass electromechanical systems using neural nets...
This paper describes the use of Fuzzy logic for the processing of EMG signals. This can increase the...
Audio and audio-pattern recognition is becoming one of the most important technologies to automatica...
[[abstract]]It has been known that fuzzy system provide a framework to handle uncertainties and vagu...
Abstract—Drive systems today determine the productivity and quality of industrial processes. However...
Pattern recognition systems play a role in applications as diverse as speech recognition, optical ch...
Some electromyogram (EMG) signals include information from limb functions and have been used to cont...
Biosignals processing, Biological Nonlinear and time-varying systems identification, Electomyograph ...
This book provides engineers and scientists in academia and industry with a thorough understanding o...
International audienceA technique is proposed that allows automatic decomposition of electromyograph...
This paper presents a novel learning algorithm for fuzzy logic neuron based on neural networks and f...
Neural networks and fuzzy techniques are among the most promising approaches to pattern recognition....
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...
The focus of this work is on the development and utilization of artificial neural networks (ANNs) fo...
Transforming noisy data into symbolic information is one of the crucial problems in syntactic patter...