Proceedings of the 2001 ACM symposium on Applied computing
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Agent-based composable simulation for virtual prototyping of fluid power system
Computers in Industry
Pulse images recognition using fuzzy neural network
Expert Systems with Applications: An International Journal
A novel parametric fuzzy CMAC network and its applications
Applied Soft Computing
An interval type-2 neural fuzzy inference system based on Piaget's action-cognitive paradigm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A hypothalamic and piagetian fuzzy inference system: HtPFIS
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
An adaptable Gaussian neuro-fuzzy classifier
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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We create a set of fuzzy rules to model a system from input-output data by dividing the input space into a set of subspaces using fuzzy partitions. We create a fuzzy rule for each subspace as the input space is being divided. These rules are combined to produce a fuzzy rule based model from the input-output data. If more accuracy is required, we use the fuzzy rule-based model to determine the structure and set the initial weights in a fuzzy neural network. This network typically trains in a few hundred iterations. Our method is simple, easy, and reliable and it has worked well when modeling large “real world” systems