Improving generalization by using genetic algorithms to determine the neural network size

2002 ◽  
Author(s):  
G. Bebis ◽  
M. Georgiopoulos
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Network Size ◽  
The Neural Network

An Efficient Neural Network Model for Path Planning of Car-like Robots in Dynamic Environment

2000 ◽  
Vol 4 (3) ◽  
pp. 220-229 ◽  
Author(s):  
Simon X. Yang ◽  
◽  
Max Meng ◽  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Dynamic Environment ◽  
Lyapunov Stability Theory ◽  
Network Size ◽  
Computationally Efficient ◽  
Neural Network Approach ◽  
The Neural Network ◽  
The Stability

In this paper, an effcient neural network approach to real-time path planning with obstacle avoidance of holonomic car-like robots in a dynamic environment is proposed. The dynamics of each neuron in this biologically inspired, topologically organized neural network is characterized by a shunting equation or an additive equation. The state space of the neural network is the configuration space of the robot. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The real-time collision-free path is planned through the dynamic neural activity landscape of the neural network without explicitly searching over neither the free workspace nor the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore it is computationally efficient. The stability of the neural network is proven by both qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency are demonstrated through simulation studies.

scholarly journals NEAT Snake: a both evolutionary and neural network adaptation approach

2020 ◽  
Author(s):  
Alisson Steffens Henrique ◽  
Vinicius Almeida dos Santos ◽  
Rodrigo Lyra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Real Time ◽  
Relative Position ◽  
Network Adaptation ◽  
The Neural Network

There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.

scholarly journals Application of Genetic Algorithms for the Selection of Neural Network Architecture in the Monitoring System for Patients with Parkinson’s Disease

2021 ◽  
Vol 11 (12) ◽  
pp. 5470
Author(s):  
Yulia Shichkina ◽  
Yulia Irishina ◽  
Elizaveta Stanevich ◽  
Armando de Jesus Plasencia Salgueiro
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Genetic Algorithms ◽  
Monitoring System ◽  
Network Architecture ◽  
Hybrid Approach ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Using Data

This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient voices and the use of genetic algorithms to select the architecture of the neural network in the monitoring system for patients with Parkinson’s disease. The process used to pre-process a person’s voice is described in order to determine the main parameters that can be used in assessing a person’s condition. It is shown that the efficiency of using genetic algorithms for constructing neural networks depends on the composition of the data. As a result, the best result in the accuracy of assessing the patient’s condition can be obtained by a hybrid approach, where a part of the neural network architecture is selected analytically manually, while the other part is built automatically.

scholarly journals Research on Network Layer Recursive Reduction Model Compression for Image Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongfei Ling ◽  
Weiwei Zhang ◽  
Yingjie Tao ◽  
Mi Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Computational Cost ◽  
Network Size ◽  
Compression Technique ◽  
Network Layer ◽  
Network Layers ◽  
Model Compression ◽  
The Neural Network

ResNet has been widely used in the field of machine learning since it was proposed. This network model is successful in extracting features from input data by superimposing multiple layers of neural networks and thus achieves high accuracy in many applications. However, the superposition of multilayer neural networks increases their computational cost. For this reason, we propose a network model compression technique that removes multiple neural network layers from ResNet without decreasing the accuracy rate. The key idea is to provide a priority term to identify the importance of each neural network layer, and then select the unimportant layers to be removed during the training process based on the priority of the neural network layers. In addition, this paper also retrains the network model to avoid the accuracy degradation caused by the deletion of network layers. Experiments demonstrate that the network size can be reduced by 24.00%–42.86% of the number of layers without reducing the classification accuracy when classification is performed on CIFAR-10/100 and ImageNet.

Study on Displacement Prediction Model of Foundation Pit

2013 ◽  
Vol 834-836 ◽  
pp. 679-682
Author(s):  
Qiang Song ◽  
Jun Jian Zhang ◽  
Yun Sheng Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Prediction Model ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Foundation Pit ◽  
Displacement Prediction ◽  
Fitness Evaluation ◽  
The Neural Network

The prediction model is proposed in this paper to predict the displacement of foundation pit. In the model, genetic algorithms is applied to optimize the node function of the neural network (15 node function coefficients are optimized simultaneously). Next, do the further optimization to the model, and GA-transFcn3 Model is established whose fitness evaluation takes into account the multi-step prediction error. Finally, it is verified that the GA-transFcn3 Model created in this article has the desirable prediction accuracy through engineering examples. The establishment of GA-transFcn3 Model can provide researchers and engineers with ideas and methods for the displacement prediction of foundation pit, and can be popularized and applied in practical projects.

Numerical Control Interpolation Algorithm of Aspheric Surface Based on the Genetic Algorithms and Neural Network

2007 ◽  
Vol 364-366 ◽  
pp. 25-29
Author(s):  
Fei Hu Zhang ◽  
D.J. Chen ◽  
L.J. Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Global Optimum ◽  
Numerical Control ◽  
Local Optimum ◽  
Interpolation Algorithm ◽  
Aspheric Surfaces ◽  
The Neural Network ◽  
Circular Curves ◽  
Circular Curve

When the Neural Network model is used to interpolate the non-circular curves, there are shortcomings of converging slowly and getting into the local optimum easily. A novel numerical control interpolation algorithm based on the GA (Genetic Algorithms) and NN (Neural Network) was introduced for the ultra-precision machining of aspheric surfaces. The algorithm integrated the global searching of GA with the parallel processing of NN, enhanceed the convergence speed and found the global optimum. At the end, the quintic non-circular curve was taken as an example to do the emulation and experiment. The results prove that this algorithm can fit the non-circular curve accurately, improve the precision of numerical control interpolation and reduce the number of calculating and interpolation cycles.

Intelligent system modelling of a three-dimensional ultrasonic positioning system using neural networks and genetic algorithms

2003 ◽  
Vol 217 (5) ◽  
pp. 367-377
Author(s):  
N Unnikrishnan ◽  
A Mahajan ◽  
T Chu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Analytical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Three Dimensional ◽  
Position Estimation ◽  
System Modelling ◽  
Dimensional System ◽  
The Neural Network

This paper presents a neural network model for a three-dimensional ultrasonic position estimation system that uses the difference in the time of arrivals of waves from a transmitter to various receivers. Even though a linearized analytical model for the three-dimensional system exists and is currently being used to estimate the position of the transmitter, its accuracy is highly dependent on complex and time consuming signal conditioning. A neural network approach is developed to train the system based on unconditioned training sets obtained directly from the receivers. It is proposed to use the final trained system to estimate the three-dimensional position in real time using these raw signals, thereby simplifying the hardware and the computational software as well as increasing the update rate. The weights of the neural network are obtained from a traditional back-progation method and by using genetic algorithms. Results for one-, two- and three-dimensional systems are presented as proof of concept. The performance of the neural network model using the raw signals is shown to be comparable to the analytical model using conditioned signals. Further, it is shown that the neural network model is extremely robust in terms of providing accurate position estimates, even after loss of information from multiple receivers. This work has significant applications in robotics, autonomous systems, virtual reality and image-guided surgery.

scholarly journals Recognition of Traffic Signs Applying Neural Network

2019 ◽  
Vol 11 (1) ◽  
pp. 145-148
Author(s):  
Zsolt Barnabás Neurohr ◽  
Edit Tóthné Laufer
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Calculation Methods ◽  
Traffic Signs ◽  
Artificial Intelligence Research ◽  
The Neural Network

Abstract Artificial intelligence is one of the most dynamically developing areas of science today. Although it is not yet an integral part of our lives to use artificial intelligence solutions, it can be seen in terms of development, that it will become available to everyone in the coming decades, and not be exclusive for the richest. An important part of artificial intelligence research are the so-called soft calculation methods, the most important of which are fuzzy logic, genetic algorithms and neural networks. In this article, the authors present a method of identifying certain traffic signs with the help of the neural network.

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