Calendar Anomalies in Athens Exchange Stock Market - An Application of GARCH Models and the Neural Network Radial Basis Function

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

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A Radial Basis Function Neural Network Approach To Determine the Survival of Listeria monocytogenes in Katiki, a Traditional Greek Soft Cheese

Journal of Food Protection ◽

10.4315/0362-028x-71.4.750 ◽

2008 ◽

Vol 71 (4) ◽

pp. 750-759 ◽

Cited By ~ 7

Author(s):

EFSTATHIOS Z. PANAGOU

Keyword(s):

Neural Network ◽

Listeria Monocytogenes ◽

Radial Basis Function ◽

Basis Function ◽

Network Approach ◽

F Test ◽

Neural Network Approach ◽

The Neural Network ◽

Radial Basis ◽

Modified Weibull

A radial basis function neural network was developed to determine the kinetic behavior of Listeria monocytogenes in Katiki, a traditional white acid-curd soft spreadable cheese. The applicability of the neural network approach was compared with the reparameterized Gompertz, the modified Weibull, and the Geeraerd primary models. Model performance was assessed with the root mean square error of the residuals of the model (RMSE), the regression coefficient (R2), and the F test. Commercially prepared cheese samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 106 CFU g −1 and stored at 5, 10, 15, and 20°C for 40 days. At each storage temperature, a pathogen viability loss profile was evident and included a shoulder, a log-linear phase, and a tailing phase. The developed neural network described the survival of L. monocytogenes equally well or slightly better than did the three primary models. The performance indices for the training subset of the network were R2 = 0.993 and RMSE = 0.214. The relevant mean values for all storage temperatures were R2 = 0.981, 0.986, and 0.985 and RMSE = 0.344, 0.256, and 0.262 for the reparameterized Gompertz, modified Weibull, and Geeraerd models, respectively. The results of the F test indicated that none of the primary models were able to describe accurately the survival of the pathogen at 5°C, whereas with the neural network all f values were significant. The neural network and primary models all were validated under constant temperature storage conditions (12 and 17°C). First or second order polynomial models were used to relate the inactivation parameters to temperature, whereas the neural network was used a one-step modeling approach. Comparison of the prediction capability was based on bias and accuracy factors and on the goodness-of-fit index. The prediction performance of the neural network approach was equal to that of the primary models at both validation temperatures. The results of this work could increase the knowledge basis for the applicability of neural networks as an alternative tool in predictive microbiology.

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Intelligent Radial Basis Function Neural Network for Intrusion Detection in Battle Field

Developments and Trends in Intelligent Technologies and Smart Systems - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-3686-4.ch008 ◽

2018 ◽

pp. 151-173

Author(s):

Kirupa Ganapathy

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

False Alarm ◽

Radial Basis Function ◽

Learning Process ◽

Basis Function ◽

Detection System ◽

The Neural Network ◽

Training Samples ◽

Radial Basis

Defense at boundary is nowadays well equipped with perimeter protection, cameras, fence sensors, radars etc. However, in battlefield there is more feasibility of entering of a non-native human and unknowing stamping of the explosives placed in the various paths by the native soldiers. There exists no alert system in the battlefield for the soldiers to identify the intruder or the explosives in the field. Therefore, there is a need for an automated intelligent intrusion detection system for battlefield monitoring. This chapter proposes an intelligent radial basis function neural network (RBFNN) technique for intrusion detection and explosive identification. The proposed intelligent RBFNN implements some intellectual components in the algorithm to make the neural network think before learning the training samples. Involvement of intellectual components makes the learning process simple, effective and efficient. The proposed technique helps to reduce false alarm and encourages timely detection thereby providing extensive support for the native soldiers and save the life of the mankind.

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Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network

Science and Technology of Nuclear Installations ◽

10.1155/2016/4720685 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 7

Author(s):

Xinyu Wei ◽

Jiashuang Wan ◽

Fuyu Zhao

Keyword(s):

Neural Network ◽

Experimental Data ◽

Radial Basis Function ◽

Basis Function ◽

Reactor Core ◽

Neuron Activity ◽

Fuel Rod ◽

The Neural Network ◽

Radial Basis ◽

Hidden Neurons

Pellet-clad interaction (PCI) is one of the major issues in fuel rod design and reactor core operation in water cooled reactors. The prediction of fuel rod failure by PCI is studied in this paper by the method of radial basis function neural network (RBFNN). The neural network is built through the analysis of the existing experimental data. It is concluded that it is a suitable way to reduce the calculation complexity. A self-organized RBFNN is used in our study, which can vary its structure dynamically in order to maintain the prediction accuracy. For the purpose of the appropriate network complexity and overall computational efficiency, the hidden neurons in the RBFNN can be changed online based on the neuron activity and mutual information. The presented method is tested by the experimental data from the reference, and the results demonstrate its effectiveness.

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Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419829961 ◽

2019 ◽

Vol 16 (2) ◽

pp. 172988141982996 ◽

Cited By ~ 6

Author(s):

Lili Wan ◽

Yixin Su ◽

Huajun Zhang ◽

Yongchuan Tang ◽

Binghua Shi

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Sliding Mode ◽

Terminal Sliding Mode ◽

Unmanned Surface Vehicle ◽

The Neural Network ◽

Fast Terminal Sliding Mode ◽

Radial Basis ◽

Unknown Disturbance

A scheme to solve the course keeping problem of the unmanned surface vehicle with nonlinear and uncertain characteristics and unknown external disturbances is investigated in this article. The chattering existing in global fast terminal sliding mode controller in solving the course keeping problem of the unmanned surface vehicle with external disturbance is analyzed. To reduce the chattering and eliminate the influence of the unknown disturbance, an adaptive global fast terminal sliding mode controller based on radial basis function neural network is developed. The equivalent control that usually requires a precise model information of the system is computed using the radial basis function neural network. The weights of the neural network are online adjusted according to the adaptive law that is derived using Lyapunov method to ensure the stability of the closed-loop system. Using the online learning of the neural network, the nonlinear uncertainty of the system and the unknown disturbance of external environment are compensated, and the system chattering is reduced effectively as well. The simulation results demonstrate that the proposed controller can achieve a good performance regarding the fast response and smooth control.

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Application of Radial Basis Function Networks Combined with Genetic Algorithm in Predicting the Geometric Parameters of Drawbead

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.101-102.790 ◽

2011 ◽

Vol 101-102 ◽

pp. 790-794

Author(s):

Jie Lai Chen ◽

Da Zhi Jiang ◽

Ya Yan Huang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Radial Basis Function ◽

Basis Function ◽

Geometric Parameters ◽

The Neural Network ◽

Fitting Method ◽

Radial Basis ◽

Curve Fitting Method ◽

Restraining Force

The drawbead plays a very important role in automobile covering part forming processes. Traditional drawbead design mainly depends on designers’ experience. In order to obtain proper restraining force during die try-out, it is often necessary to adjust the drawbead through a very complicated procedure. It is thus meaningful to study the relationship between the parameters used to reflect metal forming effects and the geometric parameters of drawbead and then create a prediction model for them. This paper employs the radial basis function neural network technology to predict the geometric parameters of drawbead used in forming processes, where the genetic algorithm is used to optimize the neural network structure. Simulation results show that the proposed approach outperforms the curve fitting method.

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