This paper presents an application of an artificial neural network to determine survival time of patients with a bladder cancer. Different learning methods have been investigated to find a solution, which is most optimal from a computational complexity point of view. In our study, a model of a multilayer perceptron with a training algorithm based on an error back-propagation method with a momen...

Snoring detection is important for diagnosing obstructive sleep apnea syndrome (OSAS) and other respiratory sleep disorders. In general, audio signal processing such as snoring sound analysis uses the frequency characteristics of the signal. Recently, a correlational filter Multilayer Perceptron neural network (f-MLP) has been proposed, which has the first hidden layer of correlational filter o...

In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...

-It is widely believed that the back propagation algorithm in neural networks, for tasks such as pattern classification, overcomes the limitations of the perceptron. We construct several counterexamples to this belief. We also construct linearly separable examples which have a unique minimum which fails to separate two families of vectors, and a simple example with four two-dimensional vectors ...

This paper introduces a flexible neural tree (FNT) with necessary number of hidden units and is generated initially as a flexible multi-layer feed-forward neural network evolved using an evolutionary procedure and also considers the approximation of sufficiently smooth multivariable function with a multilayer perceptron. For a given neural tree with approximation order, explicit formulas for th...

This paper introduces the notion of the variadic neural network (VNN). The inputs to a variadic network are an arbitrary-length list of n-tuples of real numbers, where n is fixed. In contrast to a recurrent network which processes a list sequentially, typically being affected more by more recent list elements, a variadic network processes the list simultaneously and is affected equally by all l...

In this article we are going to discuss the improvement of the multi classes’ classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes’ subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two clas...

The ideas presented here are based on two observations of perceptrons: (1) when the perceptron learning algorithm cycles among hyperplanes, the hyperplanes may be compared to select one that gives a best split of the examples, and (2) it is always possible for the perceptron to build a hyper-plane that separates at least one example from all the rest. We describe the Extentron which grows multi...