This paper compares the representational capabilities of one hidden layer and two hidden layer nets consisting of feedforward interconnections of linear threshold units. It is remarked that for certain problems two hidden layers are required, contrary to what might be in principle expected from the known approximation theorems. The differences are not based on numerical accuracy or number of un...

This paper compares the representational capabilities of one hidden layer and two hidden layer nets consisting of feedforward interconnections of linear threshold units. It is remarked that for certain problems two hidden layers are required, contrary to what might be in principle expected from the known approximation theorems. The diierences are not based on numerical accuracy or number of uni...

Limitations of one-hidden-layer perceptron networks to represent efficiently finite mappings is investigated. It is shown that almost any uniformly randomly chosen mapping on a sufficiently large finite domain cannot be tractably represented by a one-hidden-layer perceptron network. This existential probabilistic result is complemented by a concrete example of a class of functions constructed u...

In this paper, the N -bit parity problem is solved with a neural network that allows direct connections between the input layer and the output layer. The activation function used in the hidden and output layer neurons is the threshold function. It is shown that this choice of activation function and network structure leads to several solutions for the 3-bit parity problem using linear programmi...

today one of the most important problems in agriclimatology in effecting of climate on products. one of the climate elements in freezing which according to the continuity , extend and amount of it on agricultural activities ,transportation and invironment has a lot of affections. least temperature forcasts as one of the important climatic elements to forcasting late coldness and freezing in spr...

This paper presents a Fully Complex-valued Fast Learning Classifier (FC-FLC) to solve real-valued classification problems. FC-FLC is a single hidden layer network with a nonlinear input and hidden layer, and a linear output layer. The neurons at the input layer of the FC-FLC employ the circular transformation to convert the real-valued input features to the Complex domain. At the hidden layer, ...

Feature projection by non-linear discriminant analysis (NLDA) can substantially increase classification performance. In automatic speech recognition (ASR) the projection provided by the pre-squashed outputs from a one hidden layer multi-layer perceptron (MLP) trained to recognise speech subunits (phonemes) has previously been shown to significantly increase ASR performance. An analogous approac...

In this paper we developed a spiking neural network model that learns to generate online handwriting movements. The architecture is a feed forward network with one hidden layer. The input layer uses a set of Beta elliptic parameters. The hidden layer contains both excitatory and inhibitory neurons. Whereas the output layer provides the script coordinates x(t) and y(t). The proposed spiking neur...

today, there arevarious statistical models for the discrete simulation of the rainfall occurrence/non-occurrence with more emphasizing on long-term climatic statistics. nevertheless, the accuracy of such models or predictions should be improved in short timescale. in the present paper, it is assumed that the rainfall occurrence/non-occurrence sequences follow a two-layer hidden markov model (hm...

in this study, a three–layer artificial neural network (ann) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. a total of 455 data points were collected from 13 data sources to develop the ann model. superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of t...