A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions
Expert Systems with Applications: An International Journal
HebbR2-Taffic: A novel application of neuro-fuzzy network for visual based traffic monitoring system
Expert Systems with Applications: An International Journal
A novel associative memory approach to speech enhancement in a vehicular environment
Expert Systems with Applications: An International Journal
Fuzzy associative conjuncted maps network
IEEE Transactions on Neural Networks
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
R-POPTVR: a novel reinforcement-based POPTVR fuzzy neural network for pattern classification
IEEE Transactions on Neural Networks
eFSM: a novel online neural-fuzzy semantic memory model
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Diagnosis of hypoglycemic episodes using a neural network based rule discovery system
Expert Systems with Applications: An International Journal
RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
Expert Systems with Applications: An International Journal
SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System
Expert Systems with Applications: An International Journal
A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading
International Journal of Fuzzy System Applications
Hi-index | 0.01 |
The cerebellum is a brain region important for a number of motor and cognitive functions. It is able to generate error correction signals to drive learning and for the acquisition of memory skills. The cerebellar model articulation controller (CMAC) is a neural network inspired by the neurophysiologic theory of the cerebellum and is recognized for its localized generalization and rapid algorithmic computation capabilities. The main deficiencies in the basic CMAC structure are: 1) it is difficult to interpret the internal operations of the CMAC network and 2) the resolution (quantization) problem arising from the partitioning of the input training space. These limitations lead to the synthesis of a fuzzy quantization technique and the mapping of a fuzzy inference scheme onto the CMAC structure. The discrete incremental clustering (DIC) technique is employed to alleviate the quantization problem in the CMAC structure, resulting in the fuzzy CMAC (FCMAC) network. The Yager inference scheme (Yager), which possesses firm fuzzy logic foundation and maps closely to the logical implication operations in the classical (binary) logic framework, is subsequently mapped onto the FCMAC structure. This results in a novel fuzzy neural architecture known as the fuzzy cerebellar model articulation controller-Yager (FCMAC-Yager) system. The proposed FCMAC-Yager network exhibits learning and memory capabilities of the cerebellum through the CMAC structure while emulating the human way of reasoning through the Yager. The new FCMAC-Yager network employs a two-phase training algorithm consisting of structural learning based on the DIC technique and parameter learning using hebbian learning (associative long-term potentiation). The proposed FCMAC-Yager architecture is evaluated using an extensive suite of real-life applications such as highway traffic-trend modeling and prediction and performing as an early warning system for bank failure classification and medical diagnosis of breast canc- - er. The experimental results are encouraging