Fuzzy neural network approach for 2-D direction finding in multipath environments

Ma, Ching-Wen; Teng, Ching Cheng

doi:10.1049/ip-rsn:19990258

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The main advantage of FNN systems is that they can be designed using only approximation and linguistic information [1,2,[9][10][11][12][13][14][15][16]. It is also easy to design an FNN system with a satisfactory level of accuracy by choosing the network structure and learning algorithm of the FNN appropriately.…”

Section: Application In Modifying the Membership Function Of The Fnnmentioning

confidence: 99%

“…[1][2][3][9][10][11][12][13][14][15][16]. In particular, fuzzy neural network (FNN) systems (or neurofuzzy systems) have been widely applied in various fields, such as model reference control problems, PID controller tuning, signal processing, etc.…”

Section: Iintroductionmentioning

confidence: 99%

“…In particular, fuzzy neural network (FNN) systems (or neurofuzzy systems) have been widely applied in various fields, such as model reference control problems, PID controller tuning, signal processing, etc. [1][2][3][9][10][11][12][13][14][15][16]. FNN systems have the advantage that they can be designed merely based on approximation and linguistic information [1,2,13,14].…”

Section: Iintroductionmentioning

confidence: 99%

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Fine Tuning Of Membership Functions For Fuzzy Neural Systems

Lee

Teng

2001

Asian Journal of Control

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This paper presents a new method for fine-tuning the Gaussian membership functions of a fuzzy neural network (FNN) to improve approximation accuracy. This method results in special shape membership functions without the convex property. We first recall that any continuous function can be represented by a linear combination of Gaussian functions with any standard deviation. Therefore, the Gaussian membership function in the second layer of the FNN can be replaced by several small Gaussian functions; the weighting vectors of this new network (called FNN 5 ) can then be updated using the backpropagation algorithm. The proposed method can adapt proper membership functions for any nonlinear input/output mapping to achieve highly accurate approximation. Convergence analysis shows that the weighting vectors of the FNN 5 eventually converge to the optimal values. Simulation results indicate that (a) this approach improves approximation accuracy, and (b) that the number of rules can be reduced for any given level of accuracy. For the purpose of illustrating the proposed method, the FNN 5 is also applied to tune PI controllers such that gain and phase margins of the closed-loop system achieve the desired specifications.

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