Title :
Circuit realization of a programmable neuron transfer function and its derivative
Author :
Lu, Chun ; Shi, Bingxue
Author_Institution :
Inst. of Microelectron., Tsinghua Univ., Beijing, China
Abstract :
In on-chip back-propagation learning neural networks, both a sigmoidal transfer function and its derivative are required. A simple CMOS analog neuron circuit that can realizes both functions is proposed. The neuron is widely applicable because of its programmability. Based on this novel neuron, a two-layer feedforward artificial neural network (ANN) is designed. HSPICE simulation results has proved its ability to solve the XOR problem
Keywords :
CMOS analogue integrated circuits; SPICE; backpropagation; feedforward neural nets; multilayer perceptrons; transfer functions; ANN; CMOS analog neuron circuit; HSPICE simulation; XOR problem; backpropagation; circuit realization; on-chip back-propagation learning neural networks; programmable neuron transfer function derivative; sigmoidal transfer function; two-layer feedforward artificial neural network; Artificial neural networks; CMOS analog integrated circuits; Circuit simulation; Inverters; Microelectronics; Network-on-a-chip; Neurons; Piecewise linear approximation; Transfer functions; Voltage;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.860747
