To facilitate the transfer of technology emerging from theoretical research into fuzzy neural networks (FNN) for industrial applications, a fuzzy neural networks system for the automatic generation (FNNAGS) is proposed. In FNNAGS, the fuzzy model constructed by the system can be expressed as either a Mamdani model or a Takagi-Sugeno model, according to the preference of the user. Off-line design and online applications are incorporated into an interactive software system. In the stage of off-line design, only the training data need to be provided in order to construct a process model. Users do not need to give the initial fuzzy partitions, membership functions or fuzzy logic rules. These initial parameters will be set up automatically by the FNNAGS, in accordance with the properties of the training data. After off-line design has been completed, the model can be expressed as a fuzzy rule base, which can be used to control, estimate, identify or predict a process or giant through an application interface between FNNAGS and the external world.
[1]
David J. Kruglinski.
Inside Visual C
,
1993
.
[2]
Takeshi Yamakawa,et al.
A fuzzy inference engine in nonlinear analog mode and its application to a fuzzy logic control
,
1993,
IEEE Trans. Neural Networks.
[3]
Lefteri H. Tsoukalas,et al.
Fuzzy and neural approaches in engineering
,
1997
.
[4]
Jyh-Shing Roger Jang,et al.
ANFIS: adaptive-network-based fuzzy inference system
,
1993,
IEEE Trans. Syst. Man Cybern..
[5]
Chin-Teng Lin,et al.
An ART-based fuzzy adaptive learning control network
,
1997,
IEEE Trans. Fuzzy Syst..
[6]
Michio Sugeno,et al.
Fuzzy identification of systems and its applications to modeling and control
,
1985,
IEEE Transactions on Systems, Man, and Cybernetics.
[7]
Chuen-Chien Lee.
FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I
,
1990
.