Self-Organized Maps on Continuous Bump Attractors

2013 ◽  
Vol 8 (1) ◽  
pp. 234-247 ◽  
Author(s):  
К.П. Соловьева ◽  
K.P. Solovyeva
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recurrent Network ◽  
External Signal ◽  
Stable States ◽  
Factors Affecting ◽  
One Dimensional ◽  
Self Organized ◽  
The Neural Network ◽  
Winner Takes All

In this article, we describe a simple binary neuron system, which implements a self-organized map. The system consists of R input neurons (R receptors), and N output neurons of a recurrent neural network. The neural network has a quasi-continuous set of attractor states (one-dimensional “bump attractor”). Due to the dynamics of the network, each external signal (i.e. activity state of receptors) imposes transition of the recurrent network into one of its stable states (points of its attractor). That makes our system different from the “winner takes all” construction of T.Kohonen. In case, when there is a one-dimensional cyclical manifold of external signals in R-dimensional input space, and the recurrent neural network presents a complete ring of neurons with local excitatory connections, there exists a process of learning of connections between the receptors and the neurons of the recurrent network, which enables a topologically correct mapping of input signals into the stable states of the neural network. The convergence rate of learning and the role of noises and other factors affecting the described phenomenon has been evaluated in computational simulations.

Combination of Recurrent Neural Network and Deep Learning for Robot Navigation Task in Off-Road Environment

Robotica ◽  
2019 ◽  
Vol 38 (8) ◽  
pp. 1450-1462
Author(s):  
Farinaz Alamiyan-Harandi ◽  
Vali Derhami ◽  
Fatemeh Jamshidi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Robot Navigation ◽  
Recurrent Network ◽  
Navigation Task ◽  
Sensory Data ◽  
Uneven Terrain ◽  
Target Network ◽  
Fully Connected

SUMMARYThis paper tackles the challenge of the necessity of using the sequence of past environment states as the controller’s inputs in a vision-based robot navigation task. In this task, a robot has to follow a given trajectory without falling in pits and missing its balance in uneven terrain, when the only sensory input is the raw image captured by a camera. The robot should distinguish big pits from small holes to decide between avoiding and passing over. In non-Markov processes such as the abovementioned task, the decision is done using past sensory data to ensure admissible performance. Applying images as sensory inputs naturally causes the curse of dimensionality difficulty. On the other hand, using sequences of past images intensifies this difficulty. In this paper, a new framework called recurrent deep learning (RDL) with combination of deep learning (DL) and recurrent neural network is proposed to cope with the above challenge. At first, the proper features are extracted from the raw image using DL. Then, these represented features plus some expert-defined features are used as the inputs of a fully connected recurrent network (as target network) to generate command control of the robot. To evaluate the proposed RDL framework, some experiments are established on WEBOTS and MATLAB co-simulation platform. The simulation results demonstrate the proposed framework outperforms the conventional controller based on DL for the navigation task in the uneven terrains.

scholarly journals Where to put the image in an image caption generator

2018 ◽  
Vol 24 (3) ◽  
pp. 467-489 ◽  
Author(s):  
MARC TANTI ◽  
ALBERT GATT ◽  
KENNETH P. CAMILLERI
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
State Vector ◽  
Language Model ◽  
Image Features ◽  
Subsequent Stage ◽  
Multimodal Integration ◽  
The Neural Network ◽  
Large Memory ◽  
Image Caption

AbstractWhen a recurrent neural network (RNN) language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in the RNN – conditioning the language model by ‘injecting’ image features – or in a layer following the RNN – conditioning the language model by ‘merging’ image features. While both options are attested in the literature, there is as yet no systematic comparison between the two. In this paper, we empirically show that it is not especially detrimental to performance whether one architecture is used or another. The merge architecture does have practical advantages, as conditioning by merging allows the RNN’s hidden state vector to shrink in size by up to four times. Our results suggest that the visual and linguistic modalities for caption generation need not be jointly encoded by the RNN as that yields large, memory-intensive models with few tangible advantages in performance; rather, the multimodal integration should be delayed to a subsequent stage.

scholarly journals Solving the problem of classification of material properties using a neural network

2021 ◽  
Vol 2131 (3) ◽  
pp. 032084
Author(s):  
N E Babushkina ◽  
A A Lyapin
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Recurrent Neural Network ◽  
Material Properties ◽  
Practical Significance ◽  
Experimental Measurements ◽  
Statistical Parameters ◽  
The Neural Network ◽  
Main Components

Abstract The article sets the task of classifying various materials and determining their belonging to a specified group using a recurrent neural network. The practical significance of the article is to obtain the results of the neural network, confirming the possibility of classifying materials by the hardness parameter using a neural network. As part of the study, a number of experimental measurements were carried out. The structure of the neural network and its main components are described. The statistical parameters of the experimental data are estimated.

scholarly journals Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 5467
Author(s):  
Surenthiran Krishnan ◽  
Pritheega Magalingam ◽  
Roslina Ibrahim
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Prediction ◽  
The Neural Network ◽  
Proposed Model ◽  
Deep Learning Model

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

scholarly journals ESTIMATION OF THE LYAPUNOV SPECTRUM FROM ONE-DIMENSIONAL OBSERVATIONS USING NEURAL NETWORKS

2014 ◽  
pp. 30-34
Author(s):  
Vladimir Golovko
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Series ◽  
Lyapunov Spectrum ◽  
Network Approach ◽  
Neural Network Approach ◽  
One Dimensional ◽  
The Neural Network ◽  
Attractor Dynamics

This paper discusses the neural network approach for computing of Lyapunov spectrum using one dimensional time series from unknown dynamical system. Such an approach is based on the reconstruction of attractor dynamics and applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the Lyapunov spectrum of unknown dynamical system accurately and efficiently only by using one observation. The results of experiments are discussed.

scholarly journals Performance Analysis and Recognition of Speech using Recurrent Neural Network

2019 ◽  
Vol 1 (1) ◽  
pp. 87-95
Author(s):  
Bishon Lamichanne ◽  
Hari K.C.
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Performance Analysis ◽  
Recurrent Neural Network ◽  
Error Rate ◽  
English Language ◽  
Daily Lives ◽  
Crucial Step ◽  
The Neural Network ◽  
Positive Results

Speech is one of the most natural ways to communicate between people. It plays an important role in our daily lives. To make machines able to talk with people is a challenging but very useful task. A crucial step is to enable machines to recognize and understand what people are saying. Hence, speech recognition becomes a key technique providing an interface for communication between machines and humans. There has been a long research history on speech recognition. Neural network is known as a technique that has ability to classify nonlinear problem. Today, lots of research are going in the field of speech recognition with the help of the Neural Network. Even though positive results have been obtained from continuous study, research on minimizing the error rate is still gaining lots attention. The English language offers a number of challenges for speech recognition. This paper implements the RNN to analyze and recognize speech from the set of spoken words.

scholarly journals Monitoring Severe Slugging in Pipeline-Riser System Using Accelerometers for Application in Early Recognition

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3930 ◽  
Author(s):  
Sunah Jung ◽  
Haesang Yang ◽  
Kiheum Park ◽  
Yutaek Seo ◽  
Woojae Seong
Keyword(s):  
Neural Network ◽  
Linear Prediction ◽  
Early Recognition ◽  
Binary Classification ◽  
Stable State ◽  
Support Vector ◽  
Dual Frequency ◽  
Stable States ◽  
Two Phase ◽  
The Neural Network

The use of accelerometer signals for early recognition of severe slugging is investigated in a pipeline-riser system conveying an air–water two-phase flow, where six accelerometers are installed from the bottom to the top of the riser. Twelve different environmental conditions are produced by changing water and gas superficial velocities, of which three conditions are stable states and the other conditions are related to severe slugging. For online recognition, simple parameters using statistics and linear prediction coefficients are employed to extract useful features. Binary classification to recognize stable flow and severe slugging is performed using a support vector machine and a neural network. In multiclass classification, the neural network is adopted to identify four flow patterns of stable state, two types of severe slugging, and an irregular transition state between severe slugging and dual-frequency severe slugging. The performance is compared and analyzed according to the signal length for three cases of sensor location: six accelerometers, one accelerometer at the riser base, and one accelerometer at the top of the riser.

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