Conditions for Global Asymptotic Stability (GAS) of a nonlinear relaxation process realized by a Recurrent Neural Network (RNN) are provided. Existence. convergence, and robustness of such a process are analyzed. This is undertaken based upon the Contraction Mapping Theorein (CMT) and the corresponding Fixed Point Iteration (FPI). Upper bounds for such a process are shown to be the conditions o...

We propose recurrent support vector machine (RSVM) for slot tagging. This model is a combination of the recurrent neural network (RNN) and the structured support vector machine. RNN extracts features from the input sequence. The structured support vector machine uses a sequence-level discriminative objective function. The proposed model therefore combines the sequence representation capability ...

Paper presents algorithm for localization and classification of indications in the field of nondestructive defectoscopy of steam generator tubes of nuclear power plants by multifrequency eddy current method. One of the solutions how to solve this task seems to be using of recurrent neural network (RNN). Pure non-filtered signal from measurement probe was used as an input of RNN. Combination of ...

We integrate the recently proposed spatial transformer network (SPN) (Jaderberg & Simonyan, 2015) into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNNSPN to classify digits in cluttered MNIST sequences. The proposed model achieves a single digit error of 1.5% compared to 2.9% for a convolutional networks and 2.0% for convolutional networks with SPN layers. The SPN outp...

AC Accuracy ADL Activities of daily living AF Average F-measure CNN Convolutional neural network CPU Central processing unit DBN Deep belief network DT Decision tree HA Hand Gesture HAR Human activity recognition KNN K-nearest neighbors LSTM Longand short-term memory MV Means and variance NB Naive Bayes NF Normalized F-measure OAR Opportunity activity recognition RAM Random access memory ReLU R...

Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG), as a...

This paper presents a simple continuous analog hardware realization of the Random Neural Network (RNN) model. The proposed circuit uses the general principles resulting from the understanding of the basic properties of the ring neuron. The circuit for the neuron model consists only of operational ampli ers, transistors, and resistors, which makes it candidate for VLSI implementation of random n...

We report the results of our classification-based machine translation model, built upon the framework of a recurrent neural network using gated recurrent units. Unlike other RNN models that attempt to maximize the overall conditional log probability of sentences against sentences, our model focuses a classification approach of estimating the conditional probability of the next word given the in...

Recurrent Neural Networks (RNNs) have the ability to retain memory and learn data sequences. Due to the recurrent nature of RNNs, it is sometimes hard to parallelize all its computations on conventional hardware. CPUs do not currently offer large parallelism, while GPUs offer limited parallelism due to sequential components of RNN models. In this paper we present a hardware implementation of Lo...

Several authors have reported interesting results obtained by using untrained randomly initialized recurrent part of an recurrent neural network (RNN). Instead of long, difficult and often unnecessary adaptation process, dynamics based on fixed point attractors can be rich enough for further exploitation for some tasks. The principle explaining untrained RNN state space structure is called Mark...