In the past, we attempted to use a multilayer perceptron neural network as a means to prevent those unknown language inputs from being misidentified as one of the target languages in language identification system. However, the use of multilayer perceptron neural network could not utilize the temporal information from the utterances. Results show that with the use of phonemic unigram as input f...

purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learni...

Metaplasticity property of biological synapses is interpreted in this paper as the concept of placing greater emphasis on training patterns that are less frequent. A novel implementation is proposed in which, during the network learning phase, a priority is given to weight updating of less frequent activations over the more frequent ones. Modeling this interpretation in the training phase, the ...

In our previous work we proposed a Multilayer Perceptron Neural Networks (MLP NN) consisting of fuzzy flipflops (F3) based on various operations. We showed that such kind of fuzzy-neural network had good learning properties. In this paper we propose an evolutionary approach for optimizing fuzzy flip-flop networks (FNN). Various popular fuzzy operation and three different fuzzy flip-flop types w...

The authors present results of the research aiming at human identification based on tensor representation of the gait motion capture data. High-dimensional tensor samples were reduced by means of the multilinear principal component analysis (MPCA). For the purpose of classification the following methods from the WEKA software were used: k Nearest Neighbors (kNN), Naive Bayes, Multilayer Percept...

In this work we present a variational formulation for neural networks. Within this formulation, the learning problem for the multilayer perceptron lies in terms of finding a function which is an extremal for some functional. As we will see, a variational formulation for neural networks provides a direct method for the solution of general variational problems, in any dimension and up to any degr...

Reinforcement learning (RL) offers a set of various algorithms for in-situation behavior synthesis [1]. The Qlearning [2] technique is certainly the most used of the RL methods. Multilayer perceptron implementations of the Q-learning have been proposed early [3], due to the interest of the restricted memory need and the generalization capability [4]. Self-organizing map implementation of the Q-...

Microcompression tests were performed on the Al/Nb multilayers of incoherent interfaces with the layer thicknesses of 5 nm Al/5 nm Nb and 50 nm Al/50 nm Nb. The Al-Nb multilayers showed increase in strength as the layer thickness was reduced; the average flow stresses at 5% plastic strain from the 5 nm Al/5 nm Nb and 50 nm Al/50 nm Nb layer thickness specimens were determined to be 2.1 GPa and ...

The investigation of thermal and excess noise in the high-Q modes of all fused silica suspension for the Advanced LIGO test masses.. 2 1.2 The investigation of thermal and excess noise in the fused silica plates with multilayer reflective coating. .