Design of fuzzy neural networks using evolutionary algorithms
Research has been conducted on how to develop machine intelligence. Artificial Neural Networks (ANN) has been proven to mimic human brains, and has demonstrated great potential in many industrial applications. Fuzzy logic systems borrow the idea of human linguistic information processing. Evolutiona...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/10356/40884 |
| _version_ | 1826123104153239552 |
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| author | Li, Cheng Han. |
| author2 | Er Meng Joo |
| author_facet | Er Meng Joo Li, Cheng Han. |
| author_sort | Li, Cheng Han. |
| collection | NTU |
| description | Research has been conducted on how to develop machine intelligence. Artificial Neural Networks (ANN) has been proven to mimic human brains, and has demonstrated great potential in many industrial applications. Fuzzy logic systems borrow the idea of human linguistic information processing. Evolutionary Algorithms (EA) learns from another natural phenomenon, the biological evolution. All three techniques have strengths and weaknesses. The idea of hybrid systems gives birth to the Fuzzy Neural Networks (FNN), which is meant to combine the strength of both ANN and fuzzy logic systems. EA has also been experimented to work with ANN as well as FNN in some recent works. This project studies the architecture and learning algorithm design of the FNN using EA as a development tool to assist design. The project adopts the FNN architecture based on ellipsoidal basis functions and proposes a new learning algorithm. The algorithmic parameters of this algorithm are optimized by EA. Simulation studies on benchmark function approximation and identification problems have been carried out. The simulation results are compared with previous works such as the Dynamic Fuzzy Neural Networks (DFNN), the Generalized Dynamic Fuzzy Neural Networks (G-DFNN) and the Fast and Accurate Online Self-organizing Scheme for Parsimonious Fuzzy Neural Networks (FAOS-PFNN). A comparative study demonstrates the efficiency of the proposed FNN design and the potential of EA as a powerful development tool. |
| first_indexed | 2024-10-01T05:59:11Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/40884 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:59:11Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | ntu-10356/408842023-07-07T15:58:51Z Design of fuzzy neural networks using evolutionary algorithms Li, Cheng Han. Er Meng Joo School of Electrical and Electronic Engineering Centre for Intelligent Machines DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Research has been conducted on how to develop machine intelligence. Artificial Neural Networks (ANN) has been proven to mimic human brains, and has demonstrated great potential in many industrial applications. Fuzzy logic systems borrow the idea of human linguistic information processing. Evolutionary Algorithms (EA) learns from another natural phenomenon, the biological evolution. All three techniques have strengths and weaknesses. The idea of hybrid systems gives birth to the Fuzzy Neural Networks (FNN), which is meant to combine the strength of both ANN and fuzzy logic systems. EA has also been experimented to work with ANN as well as FNN in some recent works. This project studies the architecture and learning algorithm design of the FNN using EA as a development tool to assist design. The project adopts the FNN architecture based on ellipsoidal basis functions and proposes a new learning algorithm. The algorithmic parameters of this algorithm are optimized by EA. Simulation studies on benchmark function approximation and identification problems have been carried out. The simulation results are compared with previous works such as the Dynamic Fuzzy Neural Networks (DFNN), the Generalized Dynamic Fuzzy Neural Networks (G-DFNN) and the Fast and Accurate Online Self-organizing Scheme for Parsimonious Fuzzy Neural Networks (FAOS-PFNN). A comparative study demonstrates the efficiency of the proposed FNN design and the potential of EA as a powerful development tool. Bachelor of Engineering 2010-06-23T06:39:56Z 2010-06-23T06:39:56Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40884 en Nanyang Technological University 70 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Li, Cheng Han. Design of fuzzy neural networks using evolutionary algorithms |
| title | Design of fuzzy neural networks using evolutionary algorithms |
| title_full | Design of fuzzy neural networks using evolutionary algorithms |
| title_fullStr | Design of fuzzy neural networks using evolutionary algorithms |
| title_full_unstemmed | Design of fuzzy neural networks using evolutionary algorithms |
| title_short | Design of fuzzy neural networks using evolutionary algorithms |
| title_sort | design of fuzzy neural networks using evolutionary algorithms |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
| url | http://hdl.handle.net/10356/40884 |
| work_keys_str_mv | AT lichenghan designoffuzzyneuralnetworksusingevolutionaryalgorithms |