Title :
The pi-sigma network: an efficient higher-order neural network for pattern classification and function approximation
Author :
Shin, Yoan ; Ghosh, Joydeep
Author_Institution :
Dept. of Electr. & Comput Eng., Texas Univ., Austin, TX, USA
Abstract :
Introduces a novel feedforward network called the pi-sigma network. This network utilizes product cells as the output units to indirectly incorporate the capabilities of higher-order networks while using a fewer number of weights and processing units. The network has a regular structure, exhibits much faster learning, and is amenable to the incremental addition of units to attain a desired level of complexity. Simulation results show good convergence properties and accuracy for function approximation. Comparative results using the DARPA acoustic transient data set are also provided to highlight the classification abilities of pi-sigma networks
Keywords :
computerised pattern recognition; convergence; function approximation; neural nets; sonar; DARPA acoustic transient data set; accuracy; complexity; convergence properties; feedforward network; function approximation; higher-order neural network; learning; pattern classification; pi-sigma network; processing units; product cells; simulation; weights; Backpropagation; Computer networks; Convergence; Function approximation; Logic; Multilayer perceptrons; Neural networks; Noise measurement; Pattern classification; Polynomials;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155142
