Generalization Characteristics of Complex-Valued Feedforward Neural Networks in Relation to Signal Coherence

• Published in: IEEE transaction on neural networks and learning systems Vol. 23; no. 4; pp. 541 - 551
• Main Authors: Hirose, A., Yoshida, S.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Biological neural networks; Coherence; Complex-valued neural network; Computer science; control theory; systems; Computer Simulation; Connectionism. Neural networks; Exact sciences and technology; Feedback; Feedforward neural networks; function approximation; generalization; Image Interpretation, Computer-Assisted - methods; Models, Statistical; Neural networks; Neural Networks (Computer); Neurons; Pattern Recognition, Automated - methods; Signal to noise ratio; Simulation; Software; Studies; supervised learning; Tomography, Optical Coherence - methods; Vectors; Computer simulation; Supervised learning; Roundness; Activation function; Dimensional control; Radar imaging; Neural network; Feedforward; Generalization; Function approximation; Complex-valued neural network; Complex variable method

Applications of complex-valued neural networks (CVNNs) have expanded widely in recent years-in particular in radar and coherent imaging systems. In general, the most important merit of neural networks lies in their generalization ability. This paper compares the generalization characteristics of com...