Learn More
This paper describes the salient features of using a simulated annealing (SA) algorithm in the context of designing digital filters with coefficient values expressed as the sum of power of two. A procedure for linear phase digital filter design, using this algorithm, is first presented and tested, yielding results as good as known optimal methods. The(More)
This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasis on multilayer perceptron (MLP) with infinite impulse response (IIR) synapses and its variations which include generalized output and activation feedback multilayer networks (MLN's). We propose a new gradient-based procedure called recursive backpropagation(More)
—In this paper, a new complex-valued neural network based on adaptive activation functions is proposed. By varying the control points of a pair of Catmull–Rom cubic splines, which are used as an adaptable activation function, this new kind of neural network can be implemented as a very simple structure that is able to improve the generalization capabilities(More)
Acoustic source localization in the presence of reverberation is a difficult task. Conventional approaches, based on time delay estimation performed by generalized cross correlation (GCC) on a set of microphone pairs, followed by geometric triangulation, are often unsatisfactory. Prefiltering is usually adopted to reduce the spurious peaks due to(More)
This paper proposes the Blind Separation of complex signals using a novel neu-ral network architecture based on an adaptive non-linear bi-dimensional activation function; the separation is obtained maximizing the output joint entropy. Avoiding the restriction due to the Louiville's theorem, the activation function is composed by a couple of bi-dimensional(More)
This paper introduces a novel independent component analysis (ICA) approach to the separation of nonlinear convolutive mixtures. The proposed model is an extension of the well-known post nonlinear (PNL) mixing model and consists of the convolutive mixing of PNL mixtures. Theoretical proof of existence and uniqueness of the solution under proper assumptions(More)
In this paper we derive two second-order algorithms, based on conjugate gradient, for on-line training of recurrent neural networks. These azgorithms use two different techniques to extract second-order information on the Hessian matrix without calculating or storing it and without making numericaz approximations. Several simulation results for non-linear(More)
The aim of this paper is to present a new class of learning models for linear as well as non-linear neural layers called Orthonormal Strongly-Constrained SOC or Stiefel. They allow to solve orthonormal problems where orthonormal matrices are involved. After general properties of the learning rules belonging to this new class are shown, examples derived(More)