Feed-forward multilayer neural networks implementing random input–output mappings develop characteristic correlations between the activity of their hidden nodes which are important for the understanding of the storage and generalization performance of the network. It is shown how these correlations can be calculated from the joint probability distribution of the aligning fields at the hidden un...

Neural network techniques for encodlng-decoding processes have been developed. The net we have devised can work like it memory retrieval system in the sense of Hopfield, Feinstein and Palmex. Its behaviour for 2 R (R E N) input units has some special interesting features. In particular, the accsssibilities for each initial symbol may be explicitly computed. Although thermal noise may muddle the...

The main objective of this project is cell nuclei segmentation and classification. In pre-processing convert the gray scale and apply the segmentation. In segmentation this project proposes the graph cut algorithm. After segmentation the features are extracts such as texture and etc. In classification this project used the feed forward neural network. The proposed method is very efficient. The ...

In this paper, a feed forward spiking neural network is tested with spike train patterns with additional and missing spikes. The network is trained with noisy and distorted patterns with an extension of the ReSuMe learning rule to networks with hidden layers. The results show that the multilayer ReSuMe can reliably learn to discriminate highly distorted patterns spanning over 500 ms.

Design of artificial neural networks is an important and practical task:"how to choose the adequate size of neural architecture for a given application". One popular method to overcome this problem is to start with an oversized structure and then prune it to obtain simpler network with a good generalization performance. This paper presents a pruning algorithm based on pseudo-entropy of hidden n...

Neural networks (NN) are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand, this flexibility can cause overfitting and can hamper the generalization of neural networks. Many approaches to regularizing NN have been suggested but most of them are based on ad hoc arguments. Employing the principle of transformati...

Existing metrics for the learning performance of feed-forward neural networks do not provide a satisfactory basis for comparison because the choice of the training epoch limit can determine the results of the comparison. I propose new metrics which have the desirable property of being independent of the training epoch limit. The efficiency measures the yield of correct networks in proportion to...

Abstract Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified structured logical form. To address this limitation, we propose neural-symbolic framework, called Feed-Forward Neural-Symbolic Learner (FFNSL) , that integrates logic-based system capable of from noisy examples, with neural networks, order uns...

Iterative learning control is a feedforward control technique applied to systems or processes that operate in a repetitive fashion over a fixed interval of time to improve tracking/regulation performance in response to reference inputs/disturbance inputs that are repeatable in each cycle. In this paper, learning control is applied to coil-to-coil gauge and tension control during the thread-up p...

The present report summarizes the work conducted during the internship on Feedforward Control of the Magnetic Levitation Setup. Different feedforward strategies, specifically tailored for this setup, are developed and reviewed. These feedforward methods explicitly take the intrinsic position-dependent behavior of the magnetic levitation setup into account. Additionally, closed-loop stability of...