Adjustability of Neural Networks with Variant Connection Weights for Obstacle Avoidance in an Intelligent Wheelchair

2007 ◽  
Vol 11 (8) ◽  
pp. 922-930 ◽  
Author(s):  
Toshihiko Yasuda ◽  
◽  
Hajime Tanaka ◽  
Kazushi Nakamura ◽  
Katsuyuki Tanaka ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Obstacle Avoidance ◽  
Connection Weight ◽  
Powered Wheelchair ◽  
Network Connection ◽  
Intelligent Wheelchair ◽  
Numerical Studies ◽  
Mobility Aid

We have been studying electrically powered wheelchair operation to make electrically powered wheelchair intelligent and to develop a mobility aid for those who find it difficult or impossible to use conventional electrically powered wheelchairs. Some of the prototypes we have developed use neural networks providing obstacle avoidance. In previous research, we found that by varying neural network connection weight based on obstacles in the wheelchair’s vicinity and its run state, obstacle avoidance is improved. In this research, we discuss the adjustability of neural networks with variant connection weight based on numerical studies.

scholarly journals Hoax Detection System on Twitter using Feed-Forward and Back-Propagation Neural Networks Classification Method

2020 ◽  
Vol 4 (4) ◽  
pp. 655-663
Author(s):  
Crisanadenta Wintang Kencana ◽  
Erwin Budi Setiawan ◽  
Isman Kurniawan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Social Media ◽  
Detection System ◽  
Back Propagation ◽  
Detection Methods ◽  
Network Connection ◽  
The Neural Network ◽  
The World ◽  
False News

Social media is one of the ways to connect every individual in the world. It also used by irresponsible people to spread a hoax. Hoax is false news that is made as if it is true. It may cause anxiety and panic in society. It can affect the social and political conditions. This era, the most popular social media is Twitter. It is a place for sharing information and users around the world can share and receive news in short messages or called tweet. Hoax detection gained significant interest in the last decade. Existing hoax detection methods are based on either news-content or social-context using user-based features. In this study, we present a hoax detection based on FF & BP neural networks. In the developing of it, we used two vectorization methods, TF-IDF and Word2Vec. Our model is designed to automatically learn features for hoax news classification through several hidden layers built into the neural network.  The neural network is actually using the ability of the human brain that is able to provide stimulation, process, and output. It works by the neuron to process every information that enters, then is processed through a network connection, and will continue learning to produce abilities to do classification. Our proposed model would be helpful to provide a better solution for hoax detection. Data collection obtained through crawling used Twitter API and retrieve data according to the keywords and hashtags. The neural networks highest accuracy obtained using TF-IDF by 78.76%. We also found that data quality affects the performance.

Neural Control System for Autonomous Vehicles

2011 ◽  
pp. 1197-1204
Author(s):  
Francisco García-Córdova ◽  
Antonio Guerrero-González ◽  
Fulgencio Marín-García
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Neural Control ◽  
Learning Rule ◽  
Nonholonomic Mobile Robots ◽  
Neural Architecture ◽  
Neuro Controller

Neural networks have been used in a number of robotic applications (Das & Kar, 2006; Fierro & Lewis, 1998), including both manipulators and mobile robots. A typical approach is to use neural networks for nonlinear system modelling, including for instance the learning of forward and inverse models of a plant, noise cancellation, and other forms of nonlinear control (Fierro & Lewis, 1998). An alternative approach is to solve a particular problem by designing a specialized neural network architecture and/or learning rule (Sutton & Barto, 1981). It is clear that biological brains, though exhibiting a certain degree of homogeneity, rely on many specialized circuits designed to solve particular problems. We are interested in understanding how animals are able to solve complex problems such as learning to navigate in an unknown environment, with the aim of applying what is learned of biology to the control of robots (Chang & Gaudiano, 1998; Martínez-Marín, 2007; Montes-González, Santos-Reyes & Ríos- Figueroa, 2006). In particular, this article presents a neural architecture that makes possible the integration of a kinematical adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller for nonholonomic mobile robots. The kinematical adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates (García-Córdova, Guerrero-González & García-Marín, 2007). The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The obstacle avoidance adaptive neurocontroller is a neural network that learns to control avoidance behaviours in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around a cluttered environment with obstacles. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot’s sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.

scholarly journals Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

2002 ◽  
Vol 7 (1) ◽  
pp. 211
Author(s):  
Ahcene Farah ◽  
Amine Chohra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Obstacle Avoidance ◽  
Intelligent Vehicles ◽  
Approximate Reasoning ◽  
Trial And Error ◽  
Error Learning ◽  
Navigation Control ◽  
Logic System

This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN) obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.  

Detection of applicative distortions in digital images based on a combination of a neural network and a statistically optimal algorithm

2021 ◽  
Author(s):  
E.A. Samoylin ◽  
S.V. Tatarintsev ◽  
D.Yu. Dronov ◽  
K.E. Skugorov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Digital Images ◽  
Optimal Algorithm ◽  
A Priori ◽  
Average Risk ◽  
High Approximation ◽  
Approximation Properties ◽  
Second Stage ◽  
Numerical Studies

At present, when receiving and processing digital images, they often encounter cases of various noise and distortions appearing on the raster. In practice, we most often have to deal with applicative (impulsive) and additive Gaussian noise. At the same time, noise with a brightness distribution close to a truncated Gaussian, the mode of which falls on the upper or lower limits of the image brightness quantization, can be attributed to a separate class of applicative distortions, which are most often encountered in practice. At present, almost all known algorithms for correcting applicative distortions in images are spatially selective, when at the first stage of processing, the detection of distorted image elements is performed, and at the second stage, the restoration of lost (due to distortions) image elements by some method. To date, a sufficient number of algorithms for detecting (as a rule, statistical) such applicative distortions have been proposed; however, a priori unknown of the laws and parameters of the distribution of random signals and noise, as well as a priori probabilities of the presence and absence of distortions in the image, does not allow achieving their potential quality. One of the ways out of this situation can be the use of a neural network approach and its combination with statistically optimal algorithms. In the problem of detecting distortions, obtaining the necessary information about such parameters of distortions as the prior probability of their presence in the image and the standard deviation is possible through the use of artificial neural networks with high approximation properties acquired by them in the course of training on a training set of examples. This information can then be used by a statistically optimal algorithm. In accordance with this, the aim of the work is to develop a detector of applicative distortions in digital images based on a combination of a neural and statistically optimal algorithm. The essence of the proposed algorithm is to use at the first stage of processing a trained neural network capable of functioning under conditions of complete a priori uncertainty and allowing to obtain approximate estimates of a priori information about distortions in the image, and at the second stage of processing – rules based on the criterion of the minimum average risk (Bayesian criterion) using neural network estimates. As shown by the results of numerical studies, the proposed approach combines the advantage of neural networks, which consists in their high approximating properties, acquired in the course of training on precedents, as well as the optimality of a statistical detector. The presented results of numerical studies of the efficiency of the proposed detector indicate its advantage over the known detector in almost the entire possible range of applicative interference intensity.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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