Structure identification of fuzzy model
Fuzzy Sets and Systems
Fuzzy expert systems
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Wavelet applications in medicine
IEEE Spectrum
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A fast learning algorithm for parismonious fuzzy neural systems
Fuzzy Sets and Systems - Information processing
Intelligent optimal control with dynamic neural networks
Neural Networks
On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory
Neural Computing and Applications
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems (Studies in Fuzziness and Soft Computing)
Cluster and Classification Techniques for the Biosciences
Cluster and Classification Techniques for the Biosciences
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
A novel method for automated EMG decomposition and MUAP classification
Artificial Intelligence in Medicine
Classification of surface EMG signal using relative wavelet packet energy
Computer Methods and Programs in Biomedicine
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
IEEE Transactions on Information Technology in Biomedicine
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Trajectory priming with dynamic fuzzy networks in nonlinear optimal control
IEEE Transactions on Neural Networks
Computers in Biology and Medicine
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders
Computers in Biology and Medicine
A hybrid expert system approach for telemonitoring of vocal fold pathology
Applied Soft Computing
International Journal of Hybrid Intelligent Systems
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The motor unit action potentials (MUPs) in an electromyographic (EMG) signal provide a significant source of information for the assessment of neuromuscular disorders. Since recently there were different types of developments in computer-aided EMG equipment, different methodologies in the time domain and frequency domain has been followed for quantitative analysis of EMG signals. In this study, the usefulness of the different feature extraction methods for describing MUP morphology is investigated. Besides, soft computing techniques were presented for the classification of intramuscular EMG signals. The proposed method automatically classifies the EMG signals into normal, neurogenic or myopathic. Also, multilayer perceptron neural networks (MLPNN), dynamic fuzzy neural network (DFNN) and adaptive neuro-fuzzy inference system (ANFIS) based classifiers were compared in relation to their accuracy in the classification of EMG signals. Concerning the impacts of features on the EMG signal classification, different results were obtained through analysis of the soft computing techniques. The comparative analysis suggests that the ANFIS modelling is superior to the DFNN and MLPNN in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability.