A feed-forward neural network is proposed for monitoring operating modes of large scale processes. A Gaussian hidden layer associated with a Kohonen output layer map the principal features of measurements of state variables. Subsets of selective neurons are generated into the hidden layer by means of self adapting of centers and dispersions parameters of the Gaussian functions. The output layer...

Abstract. The degree of approximation by a single hidden layer MLP model with n units in the hidden layer is bounded below by the degree of approximation by a linear combination of n ridge functions. We prove that there exists an analytic, strictly monotone, sigmoidal activation function for which this lower bound is essentially attained. We also prove, using this same activation function, that...

global positioning system (gps) signals provide valuable information about ionosphere physical structure. using these signals, can be derived total electron content (tec) for each line of sight between the receiver and the satellite. for historic and other sparse data sets, the reconstruction of tec images is often performed using multivariate interpolation techniques. recently it has become cl...

The search accuracy achieved in a PDF image-plus-hiddentext (PDF-IT) document depends upon the accuracy of the optical character recognition (OCR) process that produced the searchable hidden text layer. In many cases recognising words in a blurred area of a PDF page image may exceed the capabilities of an OCR engine. This paper describes a project to replace an inadequate hidden textual layer o...

We show that a randomly selected N-tuple x of points ofRn with probability> 0 is such that any multi-layer percept ron with the first hidden layer composed of hi threshold logic units can implement exactly 2 2:~~~ ( Nil) different dichotomies of x. If N > hin then such a perceptron must have all units of the first hidden layer fully connected to inputs. This implies the maximal capacities (in t...

Infiltration rate is one of the most important parameters used in irrigation water management. Direct measurement of infiltration process is laborious, time consuming and expensive. Therefore, in this study application of some indirect methods such as artificial neural networks (ANNs) for prediction of this phenomenon was investigated. Different ANNs structures including two training algorithms...

Humans can learn its action effectively in the real world by taking a similar action in a similar situation. In order to realize such abilities in a robot with a neural network, not only generalization on the input signal space, but also the generalization based on the similarity on the reconstructed space on the hidden layer would play an important role. In this paper, to explain the acquisiti...

In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number of parameters is greater than the number of sampled points. We show that for a wide class of differentiable activation functions (this class involves “almost” all functions which are not piecewise linear), we have that...

‎Rough extreme learning machines (RELMs) are rough-neural networks with one hidden layer where the parameters between the inputs and hidden neurons are arbitrarily chosen and never updated‎. ‎In this paper‎, ‎we propose RELMs with a stable online learning algorithm for the identification of continuous-time nonlinear systems in the presence of noises and uncertainties‎, ‎and we prove the global ...