Adaptive signal processing
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
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
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptive Control
Digital Image Processing
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Fast adaptive similarity based impulsive noise reduction filter
Real-Time Imaging - Special issue on spectral imaging
A new adaptive center weighted median filter for suppressing impulsive noise in images
Information Sciences: an International Journal
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Histogram-based fuzzy colour filter for image restoration
Image and Vision Computing
Using adaptive neuro-fuzzy inference system for hydrological time series prediction
Applied Soft Computing
Performance evaluation of adaptive dual microphone systems
Speech Communication
An application of swarm optimization to nonlinear programming
Computers & Mathematics with Applications
Peer group switching filter for impulse noise reduction incolor images
Pattern Recognition Letters
Adaptive fuzzy wavelet neural network filter for hand tremor canceling in microsurgery
Applied Soft Computing
An adaptive neuro-fuzzy approach to risk factor analysis of Salmonella Typhimurium infection
Applied Soft Computing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Noise reduction by fuzzy image filtering
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
IEEE Transactions on Neural Networks
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
In this paper we propose a new self-learning complex neuro-fuzzy system (CNFS) using complex fuzzy sets (CFSs). We design a class of Gaussian complex fuzzy sets for the proposed approach. This new computing model is applied to the problem of adaptive image noise canceling (AINC), where images are corrupted additively by unknown noise and the proposed CNFS is used to adaptively perform the task of image restoration. The proposed CNFS is focused on improving image quality with additive noise. The knowledge base of CNFS is composed of Takagi-Sugeno fuzzy If-Then rules, whose premises are described by CFSs. CFS is an advanced fuzzy set, which is described by a complex-valued membership function in the unit disc of the complex plane. The utility of CFSs can enhance the non-linear functional mapping ability of the CNFS, because CFSs can carry more information into fuzzy inference computing as well as more degrees of freedom for the adaption flexibility of CNFS. For optimal estimation of the parameters of CNFS, we devise a hybrid PSO-RLSE optimization method, which combines the well-known particle swarm optimization (PSO) method and the famous recursive least squares estimation (RLSE) method. Iteratively, the PSO is used to evolve the premise parameters of CNFS, based on which the RLSE is used to update the consequent parameters. The PSO-RLSE method is very efficient for fast learning. For AINC application, the proposed CNFS is used to mimic the behavior of unknown noise channel, so that corrupted images may be adaptively restored as close to its original version as possible. Several images are used to test the proposed approach, whose experimental results are compared to other approaches. The experimental results indicate that the proposed approach outperforms the compared approaches. Excellent performance for image restoration by the proposed approach has been observed.