Frequency-based multilayer neural network with on-chip learning and enhanced neuron characteristics.
A new digital architecture of the frequency-based multilayer neural network (MNN) with on-chip learning is proposed. As the signal level is expressed by the frequency, the multiplier is replaced by a simple frequency converter, and the neuron unit uses the voting circuit as the nonlinear adder to improve the nonlinear characteristic. In addition, the pulse multiplier is employed to enhance the neuron characteristics. The backpropagation algorithm is modified for the on-chip learning. The proposed MNN architecture is implemented on field programmable gate arrays (FPGA's) and the various experiments are conducted to test the performance of the system. The experimental results show that the proposed neuron has a very good nonlinear function owing to the voting circuit. The learning behavior of the MNN with on-chip learning is also tested by experiments, which show that the proposed MNN has good learning and generalization capabilities. Simple and modular structure of the proposed MNN leads to a massive parallel and flexible network architecture, which is well suited for very large scale integration (VLSI) implementation.
Hikawa
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Department of Computer Science and Intelligent Systems, Oita University, Dannoharu 700, Oita, 870-1192, Japan.