New CMAC neural network model with adaptive quantization input layer

Xiaozhi Gao; Changhong Wang; X. M. Gao; Seppo J. Ovaska

New CMAC neural network model with adaptive quantization input layer

Xiaozhi Gao^*
, Changhong Wang
, X. M. Gao
, Seppo J. Ovaska

^*Corresponding author for this work

School of Astronautics

Harbin Institute of Technology

Research output: Contribution to conference › Paper › peer-review

7 Scopus citations

Abstract

In this paper, we first discuss the structure, principle and learning algorithm of CMAC neural network model. A new adaptive quantization method based on competitive learning is then proposed to quantize the inputs of CMAC according to the degree of variations of the approximated function. Theoretical analysis and simulation results show that with the input layer using this algorithm CMAC can approximate more accurately and efficiently than the original model using equal-size quantization method.

Original language	English
Pages	1417-1420
Number of pages	4
State	Published - 1996
Event	Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2) - Beijing, China Duration: 14 Oct 1996 → 18 Oct 1996

Conference

Conference	Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2)
City	Beijing, China
Period	14/10/96 → 18/10/96

Cite this

@conference{15da4464fa0b4453a3e69a65023105f1,

title = "New CMAC neural network model with adaptive quantization input layer",

abstract = "In this paper, we first discuss the structure, principle and learning algorithm of CMAC neural network model. A new adaptive quantization method based on competitive learning is then proposed to quantize the inputs of CMAC according to the degree of variations of the approximated function. Theoretical analysis and simulation results show that with the input layer using this algorithm CMAC can approximate more accurately and efficiently than the original model using equal-size quantization method.",

author = "Xiaozhi Gao and Changhong Wang and Gao, \{X. M.\} and Ovaska, \{Seppo J.\}",

year = "1996",

language = "英语",

pages = "1417--1420",

note = "Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2) ; Conference date: 14-10-1996 Through 18-10-1996",

}

TY - CONF

T1 - New CMAC neural network model with adaptive quantization input layer

AU - Gao, Xiaozhi

AU - Wang, Changhong

AU - Gao, X. M.

AU - Ovaska, Seppo J.

PY - 1996

Y1 - 1996

N2 - In this paper, we first discuss the structure, principle and learning algorithm of CMAC neural network model. A new adaptive quantization method based on competitive learning is then proposed to quantize the inputs of CMAC according to the degree of variations of the approximated function. Theoretical analysis and simulation results show that with the input layer using this algorithm CMAC can approximate more accurately and efficiently than the original model using equal-size quantization method.

AB - In this paper, we first discuss the structure, principle and learning algorithm of CMAC neural network model. A new adaptive quantization method based on competitive learning is then proposed to quantize the inputs of CMAC according to the degree of variations of the approximated function. Theoretical analysis and simulation results show that with the input layer using this algorithm CMAC can approximate more accurately and efficiently than the original model using equal-size quantization method.

UR - https://www.scopus.com/pages/publications/0030399077

M3 - 论文

AN - SCOPUS:0030399077

SP - 1417

EP - 1420

T2 - Proceedings of the 1996 3rd International Conference on Signal Processing, ICSP'96. Part 1 (of 2)

Y2 - 14 October 1996 through 18 October 1996

ER -

New CMAC neural network model with adaptive quantization input layer

Abstract

Conference

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