On-line adaptive quantization input space in CMAC neural network

2003 ◽  
Author(s):  
Ming-Feng Yeh ◽  
Hung-Ching Lu
Keyword(s):  
Neural Network ◽  
Input Space ◽  
Cmac Neural Network ◽  
Adaptive Quantization ◽  
On Line

Research on Load Simulator Based on CMAC Neural Network

2013 ◽  
Vol 380-384 ◽  
pp. 2169-2172 ◽  
Author(s):  
Ju Tian ◽  
Yan Ying Guo
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Feed Forward Control ◽  
Cmac Neural Network ◽  
On Line ◽  
Parallel Control ◽  
Load Simulator ◽  
Simulation Results ◽  
On Line Learning

A novel parallel control strategy based on the CMAC controller and PID control is proposed which is to solve the problem of extraneous torque which exists in the load simulator. In which, the CMAC controller realizes feed forward control, yet conventional PID controller only realizes feedback control, and using the PD algorithm improves system. The simulation results show that output of the conventional PID controller completes unceasing on-line learning by the CMAC neural network. Effectively suppress the extra torque of the load simulator, the Load Simulator system performance has been improved significantly.

scholarly journals AN ON-LINE NON-LEPTONIC NEURAL TRIGGER APPLIED TO AN EXPERIMENT LOOKING FOR BEAUTY

1994 ◽  
Vol 05 (05) ◽  
pp. 863-870
Author(s):  
C. BALDANZA ◽  
F. BISI ◽  
A. COTTA-RAMUSINO ◽  
I. D’ANTONE ◽  
L. MALFERRARI ◽  
...  
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Enrichment Factor ◽  
Data Stream ◽  
Particle Production ◽  
Event Sample ◽  
On Line

Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV π− on a Cu target, are presented. The neural trigger has been used to send on a special data stream (the Fast Stream) events to be analyzed with high priority. The non-leptonic signature uses microvertex detector data and was devised so as to enrich the fraction of events containing C3 secondary vertices (i.e, vertices having three tracks whith sum of electric charges equal to +1 or -1). The neural trigger module consists of a VME crate hosting two ETANN analog neural chips from Intel. The neural trigger operated for two continuous weeks during the WA92 1993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6–7.1 (depending on the event sample considered), which multiplied by that already provided by the standard non-leptonic trigger leads to a global C3 enrichment factor of ≈150. In the event sample selected by the neural trigger for the Fast Stream, 1 every ≈7 events contains a C3 vertex. The response time of the neural trigger module is 5.8 μs.

scholarly journals Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Bo-Jyun Liao ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Cerebellar Model Articulation Controller ◽  
Training Process ◽  
Cmac Neural Network ◽  
Photovoltaic Power ◽  
Training Samples ◽  
Parallel Connections ◽  
Model Features

This study employed a cerebellar model articulation controller (CMAC) neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

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