Detection of random vectors using an unsupervised neural network
Chuan Xi Wang, LiKang Yen, José Carlos Príncipe · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
In this paper, we consider the detection of a random vector in the presence of additive noise. First, we point out the relationship between linear optimal quadratic detector and the principal components of the random noisy signal. Then, we implement the classical quadratic detector for random signal using an adaptive unsupervised neural network. The basic element of the neural detector is the Principal Components Analysis (PCA) network proposed by Oja and Sanger. We show that the PCA network can be viewed as bandpass filter banks for noisy signals, thus it reduces the power of noise greatly. Therefore, it can improve the performance of the classical detector. The advantages of using the neural detector instead of the classical one are: (1) on-line adaptive algorithm can be used to train the neural detector; (2) noise can be reduced greatly with properly selected number of output units of the neural network; (3) parallel processing to simultaneously compute several eigenvalues of the input signal. Stimulated by this merit, it has been pointed out that neural networks can be used for most signal processing problems based on subspace decomposition.