Abstract:
The author shows that an additive fuzzy system can approximate any continuous function on a compact domain to any degree of accuracy. Fuzzy systems are dense in the space...Show MoreMetadata
Abstract:
The author shows that an additive fuzzy system can approximate any continuous function on a compact domain to any degree of accuracy. Fuzzy systems are dense in the space of continuous functions. The fuzzy system approximates the function by covering its graph with fuzzy patches in the input-output state space. Each fuzzy rule defines a fuzzy patch and connects commonsense knowledge with state-space geometry. Neural or statistical clustering algorithms can approximate the unknown fuzzy patches and generate fuzzy systems from training data.<>
Date of Conference: 08-12 March 1992
Date Added to IEEE Xplore: 06 August 2002
Print ISBN:0-7803-0236-2
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Fuzzy System ,
- Space Of Functions ,
- Triangular ,
- Adaptive System ,
- Binary Data ,
- Learning System ,
- Membership Function ,
- Fuzzy Set ,
- Production Of Space ,
- Uniform Approximation ,
- Expert System ,
- Conditions Hold ,
- Fuzzy Rules ,
- Vector Quantization ,
- Unit Cube ,
- Approximation Theorem ,
- Fuzzy Variables ,
- Universal Type ,
- Symmetric Set
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Fuzzy System ,
- Space Of Functions ,
- Triangular ,
- Adaptive System ,
- Binary Data ,
- Learning System ,
- Membership Function ,
- Fuzzy Set ,
- Production Of Space ,
- Uniform Approximation ,
- Expert System ,
- Conditions Hold ,
- Fuzzy Rules ,
- Vector Quantization ,
- Unit Cube ,
- Approximation Theorem ,
- Fuzzy Variables ,
- Universal Type ,
- Symmetric Set
