This paper proposes an algorithm for detection in multiple input multiple output (MIMO) communication systems. The algorithm combines minimum mean square error (MMSE) and minimum mean square error with ordered successive interference cancellation (MMSE-OSIC) detectors with detection error estimation. Neyman-Pearson criterion is used and the decision boundary is derived. It helps the implementat...

Component extraction is a technique for extracting the latent components that underlie the observation of a set of variables. In the paper both classical Principal component analysis (PCA) and autoassociative principal component neural network (PCNN) methods with minimum mean square error (MSE) criterion are compared with the corresponding extended component extraction methods with Minimum erro...

In this paper, we propose a procedure for training a pronunciation network with criteria consistent with the optimality objectives for speech recognition systems. In particular, we describe a framework for using maximum likelihood(ML) and minimum classi cation error(MCE) criteria for pronunciation network optimization. The ML criterion is used to obtain an optimal structure for the pronunciatio...

We study several variations of the question minimum-error discrimination thermal states. Besides providing optimal values for probability error, we also characterize measurements. For case a fixed Hamiltonian, show that general problem measurement is in energy basis Hamiltonian. identify critical temperature, determining whether given temperature best distinguishable from state very high or low...

The feedforward neural network architecture uses backpropagation learning to determine optimal weights between different interconnected layers. This learning procedure uses a gradient descent technique applied to a sum-of-squares error function for the given inputoutput pattern. It employs an iterative procedure to minimise the error function for a given set of patterns, by adjusting the weight...