FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity

Nie, Junhong; Linkens, D.A.

doi:10.1016/0005-1098(94)90154-6

Cited by 61 publications

(27 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…into association unit, so it has the fuzzy logic property. At the same time, it uses CMAC addressing method as mapping, so the input space can be demarcated more fine, which is different from conventional fuzzy CMAC [3,4,5]. Therefore, this network is called general fuzzy CMAC, abbreviated as GFAC.…”

Section: Gfac Structure and Its Learning Algorithm A Network Strmentioning

confidence: 99%

Recursive Least-squares Reinforcement Learning Controller Based on General Fuzzy CMAC

Shen

Zhang

Guo

2007

2007 Second International Conference on Bio-Inspired Computing: Theories and Applications

View full text Add to dashboard Cite

Combined CMAC addressing schemes with fuzzy logic idea, a general fuzzy CMAC (GFAC) is proposed, in which the fuzzy membership functions are utilized as the receptive field functions. The mapping of receptive field functions, the selection law of membership function and the learning algorithm are presented. Recursive least-squares temporal difference algorithm (RLS-TD) is deduced, which can use data more efficiently with fast convergence and less computational burden. Using RLS-TD method a reinforcement learning structure based on GFAC is applied to ship steering control, as provides an efficient way for the improvement of ship steering control performance. The parameters of controller are online learned and adjusted. Simulation results show that the ship course can be properly controlled in case of the disturbances of wave and wind. It is demonstrated that the proposed algorithm is a promising alternative to conventional autopilots.

show abstract

Section: Gfac Structure and Its Learning Algorithm A Network Strmentioning

confidence: 99%

Recursive Least-squares Reinforcement Learning Controller Based on General Fuzzy CMAC

Shen

Zhang

Guo

2007

2007 Second International Conference on Bio-Inspired Computing: Theories and Applications

View full text Add to dashboard Cite

show abstract

“…In order to improve the accuracy and real time character of CMAC, on the network output calculating stage, document [13,16] and [17] absorbed fuzzy self-organization competing algorithm to restructure the conventional CMAC neural network. The following definitions are made: Definition 1 assume that the N L hypercube addressed by a certain input x in CMAC can be recognized as a subspace j which is centered at z j and has a width of 2δ, we call j as association field.…”

Section: Fuzzy Cmac Neural Networkmentioning

confidence: 99%

Unmanned Underwater Vehicles Fault Identification and Fault-Tolerant Control Method Based on FCA-CMAC Neural Networks, Applied on an Actuated Vehicle

Liu

Zhu

Yang

2011

J Intell Robot Syst

View full text Add to dashboard Cite

A novel fault diagnosis and accommodation method for unmanned underwater vehicles thruster is presented in this paper. FCA-CMAC (Credit Assignment-based Fuzzy Cerebellar Model Articulation Controllers) neural network is used to realize the fault identification for thruster continuous and uncertain jammed fault situation. A reconstruction algorithm based on weighted pseudo-inverse is used to find the available solution of the control allocation problem. To illustrate effective of the proposed method, two simulation examples of multi-uncertain abrupt faults are given in the paper.

show abstract

“…In order to improve the accuracy and real time character of CMAC, on the network output calculating stage, Nie and Linkens (1994) absorbed the fuzzy self-organization competing algorithm to the conventional CMAC neural network.…”

Section: The Fuzzy Cmac Neural Networkmentioning

confidence: 99%

“…It is known that two kinds of operating are included in the conventional CMAC, one is the calculating output result and the other is the learning and adjusting weight. Recently, in the first operation, several approaches have been proposed to improve the learning performance of the CMAC algorithm (Nie andLinkens 1994, Geng andMccullough 1997), they introduced the fuzzy concept into the cell structure of CMAC (FCMAC); in the second operation, Shun et al presented a credit assigned CMAC learning approach (CA-CMAC), which uses the learning times of the addressed hypercubes as the credibility, and its online learning capability can be improved, especially in the beginning of learning stages (Shun et al 2003). However, for online fault-tolerant control of non-linear dynamic systems, the convergence speed of fault learning still needs to be improved further, in order to satisfy the requirement for realtime applications.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fault-tolerant control of non-linear systems: an improved CMAC-based fault learning approach

Zhu

Kang

2007

International Journal of Control

View full text Add to dashboard Cite

The conventional cerebellar model articulation controllers (CMAC) learning scheme equally distributes the correcting errors into all addressed hypercubes, regardless of the credibility of those hypercubes. This paper presents the adaptive fault-tolerant control scheme of nonlinear systems using a fuzzy credit assignment CMAC neural network online fault learning approach. The credit assignment concept is introduced into fuzzy CMAC weight adjusting to use the learned times of addressed hypercubes as the credibility of CMAC. The correcting errors are proportional to the inversion of learned times of addressed hypercubes. With this fault learning model, the learning speed of fault can be improved. After the unknown fault is estimated, online, by using the fuzzy credit assignment CMAC, the effective control law reconfiguration strategy based on the sliding mode control technique is used to compensate for the effect of the fault. The proposed fault-tolerant controller adjusts its control signal by adding a corrective sliding mode control signal to confine the system performance within a boundary layer. The numerical simulations demonstrate the effectiveness of the proposed CMAC algorithm and fault-tolerant controller.

show abstract

FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity

Cited by 61 publications

References 20 publications

Recursive Least-squares Reinforcement Learning Controller Based on General Fuzzy CMAC

Recursive Least-squares Reinforcement Learning Controller Based on General Fuzzy CMAC

Unmanned Underwater Vehicles Fault Identification and Fault-Tolerant Control Method Based on FCA-CMAC Neural Networks, Applied on an Actuated Vehicle

Adaptive fault-tolerant control of non-linear systems: an improved CMAC-based fault learning approach

Contact Info

Product

Resources

About