Browsing by Author "Tsai, Jian-Feng"

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Design and performance analysis of an axial-flux disk-type switched reluctance motor for hybrid scooters
(2006-09-01)
Tsai, Jian-Feng
;
Chen, Yon-Ping
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Switched reluctance motors (SRM) possess several superiorities such as simplicity in structure, ruggedness, and good reliability, which make it viable for the transportation applications. In this paper, an axial-flux disk-type switched reluctance motor (DSRM) for hybrid scooters is proposed. The DSRM can be used to drive the scooter and as an engine starter simultaneously. Moreover, the torque density of axial flux motors is more superior to that of radial flux motors in this application. Instead of utilizing FEM, the permeance model is employed to model the DSRM. With the analytical expressions, the output torque and current can be obtained dynamically and the geometries of the DSRM can be further optimized. Finally, the hardware-in-the-loop (HIL) simulation environment is constructed to perform full system simulation and the DSRM is shown to be capable of being an auxiliary power source for hybrid scooters.
Symbiotic Structure Learning Algorithm for Feedforward Neural-Network-Aided Grey Model and Prediction Applications
(2017-01-01)
Yang, Shih-Hung
;
Huang, Wun-Jhu
;
Tsai, Jian-Feng
;
Chen, Yon-Ping
;
電子工程學系及電子研究所
The learning ability of neural networks (NNs) enables them to solve time series prediction problems. Off-line training can be applied to design the structure and weights of NNs when sufficient training data are available. However, this may be inadequate for applications that operate in real time, possess limited memory size, or require online adaptation. Furthermore, the structural design of NNs (i.e., the number of hidden neurons and connected topology) is crucial. This paper presents a novel algorithm, called the symbiotic structure learning algorithm (SSLA), to enhance a feedforward neural-network-aided grey model (FNAGM) for real-time prediction problems. Through symbiotic evolution, the SSLA evolves neurons that cooperate well with each other, and constructs NNs from the neurons with hyperbolic tangent and linear activation functions. During construction, the hidden neurons with the linear activation function can be simplified to a few direct connections from the inputs to the output neuron, leading to a compact network topology. The NNs share the fitness value with participating neurons, which are further evolved through neuron crossover and mutation. The proposed SSLA was evaluated through three real-time prediction problems. Experimental results showed that the SSLA-derived FNAGM possesses a partially connected NN with few hidden neurons and a compact topology. The evolved FNAGM outperforms other methods in prediction accuracy and continuously adapts the NN to the dynamic changes of the time series for real-time applications.