A reduced and comprehensible polynomial neural network for classification
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology
IEEE Transactions on Neural Networks
Neural network synthesis dealing with classification problem
ACMOS'11 Proceedings of the 13th WSEAS international conference on Automatic control, modelling & simulation
A prototype classifier based on gravitational search algorithm
Applied Soft Computing
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
An efficient CMAC neural network for stock index forecasting
Expert Systems with Applications: An International Journal
Proceedings of the 15th annual conference on Genetic and evolutionary computation
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
| Hi-index | 0.00 |
Developing an efficient classification method is a challenge task in many research domains, such as neural network (NN) classifiers, statistical classifiers and machine learning. This study focuses on NN classifiers, which are data-driven analytical techniques. This study presents a cerebellar model articulation controller NN (CMAC NN) classifier, which has the advantages of very fast learning, reasonable generalization ability and robust noise resistance. To increase the accuracies of training and generalization, the CMAC NN classifier is designed with multiple-input and multiple-output (MIMO) network topology. The performance of the proposed MIMO CMAC NN classifier is evaluated using PROBEN1 benchmark datasets (such as for diabetes, cancer and glass) taken from the UCI Machine Learning Repository. Numerical results indicate that the proposed CMAC NN classifier is efficient for tested datasets. Moreover, this study compares the experimental results of the CMAC NN classifier with those in the published literature, indicating that the CMAC NN classifier is superior to some published classifiers. Therefore, the CMAC NN classifier can be considered as an analytical tool for solving classification tasks, such as medical decision making.