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—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)

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)

A large class of nonlinear dynamic adaptive systems such as dynamic recurrent neural networks can be effectively represented by signal flow graphs (SFGs). By this method, complex systems are described as a general connection of many simple components, each of them implementing a simple one-input, one-output transformation, as in an electrical circuit. Even… (More)

Neural networks with internal temporal dynamic can be applied to non-linear DSP problems. The classical fully connected recurrent architectures, can be replaced by less complex neural networks, based on the well known MultiLayer Perceptron (MLP) where the temporal dynamic is modelled by replacing each synapses either with a FIR filter or with an IIR filter.… (More)

In this paper we propose a new learning algorithm for locally recurrent neural networks, called Truncated Recursive Back Propagation which can be easily implemented on-line with good performance. Moreover it generalises the algorithm proposed by Waibel et al. for TDNN, and includes the Back and Tsoi algorithm as well as BPS and standard on-line Back… (More)

This paper presents a new technique to control stability of IIR adaptive filters based on the idea of intrinsically stable operations that makes possible to continually adapt the coefficients with no need of stability test or poles projection. The coefficients are adapted in a way that intrinsically assures the poles to be in the unit circle. This makes… (More)

We derive a new class of neural unsupervised learning rules which arises from the analysis of the dynamics of an abstract mechanical system. The corresponding algorithms can be used to solve several problems in Digital Signal Processing area, where orthonormal matrices are involved. We present an application which deals with blind separation of sources,… (More)

1 Abstract A large class of non-linear dynamic adaptive systems such as dynamic recurrent neural networks can be very eeectively represented by Signal-Flow-Graphs SFGs. By this method, complex systems are described as a general connection of many simple components, each of them implementing a simple one-input one-output transformation, as in an electrical… (More)

Linear Recursive Filters and also Recurrent Neural Networks can be adapted on-line but sometimes with instability problems. Stability control techniques exist for the linear case but they are either computationally expensive or non-robust. For the non-linear case, stability control is simply usually not performed in applications. This paper presents a new… (More)