In this paper, we present a comparative study of several methods that combine evolutionary algorithms and local search methods to optimize multilayer perceptrons: A method that optimizes the architecture and initial weights of multilayer perceptrons; another that searches for training algorithm parameters, and finally, a co-evolutionary algorithm, introduced in this paper, that handles the arch...

In this paper, a key swap over mechanism among group of multilayer perceptrons for encryption/decryption (KSOGMLPE) has been proposed in wireless communication of data/information. Two parties can swap over a common key using synchronization between their own multilayer perceptrons. But the problem crop up when group of N parties desire to swap over a key. Since in this case each communicating ...

Despite the recent success of deep neural networks in various applications, designing and training deep neural networks is still among the greatest challenges in the field. In this work, we address the challenge of designing and training feedforward Multilayer Perceptrons (MLPs) from a smooth optimisation perspective. By characterising the critical point conditions of an MLP based loss function...

Neural network process modelling needs the use of experimental design and studies. A new neural network constructive algorithm is proposed. Moreover, the paper deals with the influence of the parameters of radial basis function neural networks and multilayer perceptrons network in process modelling. Particularly, it is shown that the neural modelling, depending on learning approach, cannot be a...

Multilayer feed-forward networks, or multilayer perceptrons (MLPs) have one or several " hidden " layers of nodes. This implies that they have two or more layers of weights. The limitations of simple perceptrons do not apply to MLPs. In fact, as we will see later, a network with just one hidden layer can represent any Boolean function (including the XOR which is, as we saw, not linearly separab...

This paper discusses the ability of multilayer perceptrons (MLPs) to model the probability distribution of data in typical pattern recognition and verification problems. It is proven that multilayer perceptrons with sigmoidal units and a number of hidden units less or equal than the number of inputs are unable to model patterns distributed in typical clusters, since these networks draw open sep...

Multilayer perceptrons (MLPs) are widely used for pattern classification and regression problems. Backpropagation (BP) algorithm is known technique in the training of multilayer perceptrons. However for its optimum training convergence, the learning and momentum parameters need to be tuned on trial and error method. Further, sometimes the backpropagation algorithm fails to achieve global conver...

In this paper a Neural Networks (NN) is proposed for transient stability prediction. Transient stability of a power system is first determined based on the generator relative rotor angles procured from time domain simulation outputs. Simulations were carried out on a single machine infinite bus system by considering three phase short circuit fault on the system. The data collected from the time...

In this paper, a key generation and certification technique using multilayer perceptron (KGCMLP) has been proposed in wireless communication of data/information. In this proposed KGCMLP technique both sender and receiver uses an identical multilayer perceptrons. Both perceptrons are start synchronization by exchanging some control frames. During synchronization process message integrity test an...

Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand underestimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep ne...