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24752. Neural network world: Optimized spiral spherical SOM (OSS-SOM)
- Creator:
- Jagric, Timotej and Zunko, Matjaz
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Self-organizing map, spherical SOM, border effect, and topology
- Language:
- English
- Description:
- The border effect is one of the problems, which can appear in the application of self-organizing maps (SOM). Different solutions were presented in the literature, but each of them has its drawbacks. In this paper we present a new method for overcoming the border effect - optimized spiral spherical SOM. We also show that standard measure of irregularity is not appropriate and present a modified version - Gaussian measure of irregularity. Our simulations suggest that the new variant of SOM achieves extremely low values of irregularity in comparison to other methods. At the end of the paper we present a software solution for the proposed method.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
24753. Neural network-based autor-tuning for PID controllers
- Creator:
- Rivas-Echeverría, Francklin, Ríos-Bolívar, Addison, and Casales-Echeverría, Jeanette
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- PID, neural networks (NN), auto-tuning, and integrál error criteria
- Language:
- English
- Description:
- PID controllers have become the most popular control strategy in indiistrial processes due to tlie versatility and tumiiiig capabilities. The iiicorporation of aiito-tunning tools have increased the use of this kind of controllers. In this paper we i)ropose a neural network-based self-tunning scherne for on-line updating of PID parameters, which is based on integ'ral error criteria (lAE, ISE, ITAE, ITSE).
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
24754. Neural networks - package for mathematica software
- Creator:
- Zelinka, Ivan
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Language:
- English
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
24755. Neural networks application for mechanical parameters identification of asynchronous motor
- Creator:
- Balara, D., Timko , J., Žilková , J., and Lešo, M.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- neural network, identification, and electric drive
- Language:
- English
- Description:
- A method for identification of mechanical parameters of an asynchronous motor is presented in this paper. The identification method is based on the use of our knowledge of the system. This paper clarifies the method by using the example identifying of mechanical parameters of the three-phase squirrel-cage asynchronous motor.A model of mechanical subsystem of the motor is presented as well as results of simulation. The special neural network is used as an identification model and its adaptation is based on the gradient descent method.The parameters of mechanical subsystem are derived from the values of synaptic weights of the neural identification model after its adaptation. Deviation of identified mechanical parameters in the case of moment inertia was up to 0.03% and in the case of load torque was 1.45% of real values.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
24756. Neural networks based screening of real estate transactions
- Creator:
- Kontrimas , Vilius and Verikas, Antanas
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Support vector machine, multilayer perceptron, self organizing map, outlier detection, and real estate
- Language:
- English
- Description:
- Aiming to hide the real money gains and to avoid taxes, fictive prices are sometimes recorded in the real estate transactions. This paper is concerned with artificial neural networks based screening of real estate transactions aiming to categorize them into "clear" and "fictitious" classes. The problem is treated as an outlier detection task. Both unsupervised and supervised approaches to outlier detection are studied here. The soft minimal hyper-sphere support vector machine (SVM) based novelty detector is employed to solve the task without the supervision. In the supervised case, the effectiveness of SVM, multilayer perceptron (MLP), and a committee based classification of the real estate transactions are studied. To give the user a deeper insight into the decisions provided by the models, the real estate transactions are not only categorized into "clear" and "fictitious" classes, but also mapped onto the self organizing map (SOM), where the regions of "clear", "doubtful" and "fictitious" transactions are identified. We demonstrate that the stability of the regions evolved in the SOM during training is rather high. The experimental investigations performed on two real data sets have shown that the categorization accuracy obtained from the supervised approaches is considerably higher than that obtained from the unsupervised one. The obtained accuracy is high enough for the technique to be used in practice.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
24757. Neural networks in statistical anomaly intrusion detection
- Creator:
- Zhang, Zheng and Manikopoulos, Constatine
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- security, intrusion detection, statistical anomaly detection, neural network classification, perceptron, backpropagation, perceptron-backpropagation- hybrid, fuzzy ARTMAP, and radial-based function
- Language:
- English
- Description:
- In this paper, we report on experiments in which we used neural networks for statistical anomaly intrusion detection systems. The five types of neural networks that we studied were: Perceptron; Backpropagation; Perceptron-Backpropagation-Hybrid; Fuzzy ARTMAP; and Radial-Based Function. We collected four separate data sets from different simulation scenarios, and these data sets were used to test various neural networks with different hidden neurons. Our results showed that the classification capabilities of BP and PBH outperform those of other neural networks.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
24758. Neural networks training by artificial bee colony algorithm on pattern classification
- Creator:
- Karaboga, Dervis and Ozturk, Celal
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Neural network training, artificial bee colony algorithm, and pattern classification
- Language:
- English
- Description:
- Artificial Neural Networks are commonly used in pattern classification, function approximation, optimization, pattern matching, machine learning and associative memories. They are currently being an alternative to traditional statistical methods for mining data sets in order to classify data. Artificial Neural Networks are well-established technology for solving prediction and classification problems, using training and testing data to build a model. However, the success of the networks is highly dependent on the performance of the training process and hence the training algorithm. In this paper, we applied the Artificial Bee Colony (ABC) Optimization Algorithm on training feed-forward neural networks to classify different data sets which are widely used in the machine learning community. The performance of the ABC algorithm is investigated on benchmark classification problems from classification area and the results are compared with the other well-known conventional and evolutionary algorithms. The results indicate that ABC algorithm can efficiently be used on training feed-forward neural networks for the purpose of pattern classification.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
24759. Neural tissue response to impact - numerical study of wave propagation at level of neural cells
- Creator:
- Hozman, Jiří, Bradáč , Josef, and Kovanda, Jan
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Wave propagation in neural medium, discontinuous Galerkin method, Crank-Nicolson shceme, high-resolution semi-implict scheme, traveling wave, energy invariant, Gauss pulse, and critical frequency
- Language:
- English
- Description:
- In this article, we deal with a numerical solution of the issue concerning one-dimensional longitudinal mechanical wave propagation in linear elastic neural weakly heterogeneous media. The crucial idea is based on the discretization of the wave equation with the aid of a combination of the discontinuous Galerkin method for the space semi-discretization and the Crank-Nicolson scheme for the time discretization. The linearity of the second-order hyperbolic problem leads to a solution of a sequence of linear algebraic systems at each time level. The numerical experiments performed for the single traveling wave and Gauss initial impact demonstrate the high-resolution properties of the presented numerical scheme. Moreover, a well-known linear stress-strain relationship enables us to analyze a high-frequency regime for the initial excitation impact with respect to strain-frequency dependency.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
24760. Neural-network-based genetic algorithm for optimal kitchen faucet styles
- Creator:
- Ozsoydan, F. B., Kandemir , C. M. , and Demirtas , E. A.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- kitchen faucets, styling, artificial neural networks, and genetic algorithms
- Language:
- English
- Description:
- Artificial neural networks (ANNs) are the models of choice in many data classification tasks. In this study, ANN classification models were used to explore user perceptions about kitchen faucet styles and investigate the relations between the overall preferences and kansei word scores of users. The scores given by consumers were obtained via a two-stage questionnaire mentioned in a previous study by the authors. Through the questionnaire, consumers were asked to give scores after examining three-dimensional (3-D) drawings of new product samples created with the help of industrial product designers. Because it was neither practical nor necessary to develop a prototype or a picture of each of the alternative designs, a fractional factorial experimental design similar to Taguchi's L-16 orthogonal array was used. After completing this preparatory work to develop ANNs and obtain the necessary related data, an analysis of variance (ANOVA) was performed to identify the critical factors that affect the accuracy of the ANN model to be used and determine the best factor levels for the ANN model. A genetic algorithm (GA) was then integrated with the ANN model found to be the best and implemented to determine the optimal levels of the design parameters related to product appearance. Lastly, the product categories were classified as unfavorable or favorable, and three products were derived for each category. In comparison with the previously published papers of the authors, the GA integrated with the ANN model was found to be an effective tool for revealing user perceptions in new product development. In regard to the findings of the present work, it can be said that, this technique can be used as an alternative of several complex analytical approaches, in order to explore users' perceptions.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public