FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling

Russo, Marco

doi:10.1109/91.705506

Cited by 148 publications

(59 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The roulette wheel selection method [19] is used to select chromosomes for operation. The chance on the roulette wheel is adaptive and is given as , where (8) and is the performance of the model encoded in chromosome measured in terms of the mean-squared-error (mse) (9) where is the true output and is the model output. The inverse of the selection function is used to select chromosomes for deletion.…”

Section: B Selection Functionmentioning

confidence: 99%

“…Also, GAs have been combined with other techniques like fuzzy clustering [5], [6], neural networks [7], [8], statistical information criteria [9], Kalman filters [9], hill climbing [8] and even fuzzy expert control of the GAs operators [10], to mention some. This has resulted in many complex algorithms and, as recognized in [11] and [12], often the transparency and compactness of the resulting rule base is not considered to be of importance.…”

mentioning

confidence: 99%

See 1 more Smart Citation

GA-fuzzy modeling and classification: complexity and performance

Setnes

Roubos

2000

IEEE Trans. Fuzzy Syst.

436

219

View full text Add to dashboard Cite

Abstract-The use of genetic algorithms (GAs) and other evolutionary optimization methods to design fuzzy rules for systems modeling and data classification have received much attention in recent literature. Authors have focused on various aspects of these randomized techniques, and a whole scale of algorithms have been proposed. We comment on some recent work and describe a new and efficient two-step approach that leads to good results for function approximation, dynamic systems modeling and data classification problems. First fuzzy clustering is applied to obtain a compact initial rule-based model. Then this model is optimized by a real-coded GA subjected to constraints that maintain the semantic properties of the rules. We consider four examples from the literature: a synthetic nonlinear dynamic systems model, the iris data classification problem, the wine data classification problem, and the dynamic modeling of a diesel engine turbocharger. The obtained results are compared to other recently proposed methods.

show abstract

Section: B Selection Functionmentioning

confidence: 99%

mentioning

confidence: 99%

GA-fuzzy modeling and classification: complexity and performance

Setnes

Roubos

2000

IEEE Trans. Fuzzy Syst.

436

219

View full text Add to dashboard Cite

show abstract

“…EAs can optimize both the architecture and parameters of neurofuzzy systems. Put it alternatively, EAs are used to evolve both the fuzzy rules and their respective MFs and connection weights [146][147][148][149][150][151]. The fuzzy neural network [146] has the architecture of a standard two-level OR/AND representation of Boolean functions of symbols.…”

Section: Constructing Neurofuzzy Systems Using Evolutionary Algorithmsmentioning

confidence: 99%

“…The architectural optimization is then performed by using the GP, whereas the ensuing parameters are optimized by gradient-based learning. The fuzzy genetic neural system (FuGeNeSys) [147] and the genetic fuzzy rule extractor (GEFREX) [148] are synergetic models of fuzzy logic, neural networks, and the GA. They both are general methods for fuzzy supervised learning of multiple-input multiple-output (MIMO) systems, wherein the Mamdani fuzzy model is used.…”

Section: Constructing Neurofuzzy Systems Using Evolutionary Algorithmsmentioning

confidence: 99%

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Zhang¹,

Wu²,

et al. 2011

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neoDarwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

show abstract

“…The int,egration of neural networks and the fuzzy set theory results in a classifier that has useful properties of both neural networks and fuzzy sets. The combination of neural networks and fuzzy sets fonns a synergetic network that handles pattern classification problems very effectively and efficiently [24,57,77,107,108].…”

Section: Introductionmentioning

confidence: 99%

Pattern classification by an incremental learning fuzzy neural network

Yen

Meesad

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

View full text Add to dashboard Cite

PREFACETo detect and identify defects in machine condition health monitoring, classical neural classifiers, such as Multilayer Perceptron (MLP) neural networks, are proposed to supervise the monitored system. A drawback of classical neural classifiers, off-line and iterative learning algorithms, is a long training time. In addition, they are often stuck at local minima, unable to achieve the optimum solution. Furthennore, in an operating mode, it is possible that new faults are developing while a monitored system is running.These new classes of defects need to be instantly detected and distinguished from those Training by various torque levels, the network achieved 100% correct prediction for the same torque level of testing data. Furthermore, the classification performance of the network has been tested using other benchmark data, such as the Fisher's Iris data, the two-spiral problem, and a vowel data set. Comparison studies among other well-known classifiers were preformed. The ILFN was found competitive with or even superior to many classifiers.tV ACKNOWLEDGMENTS

show abstract

FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling

Cited by 148 publications

References 27 publications

GA-fuzzy modeling and classification: complexity and performance

GA-fuzzy modeling and classification: complexity and performance

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Pattern classification by an incremental learning fuzzy neural network

Contact Info

Product

Resources

About