An Elman Model Based on GMDH Algorithm for Exchange Rate Forecasting

Since the Elman Neural Networks was proposed, it has attracted wide attention. This method has fast convergence and high prediction accuracy. In this study, a new hybrid model that combines the Elman Neural Networks and the group method of data handling (GMDH) is used to forecast the exchange rate. The GMDH algorithm is used for system modeling. Input variables are selected by the external standards. Based on the output of the GMDH algorithm, valid input variables can be used as an input for the Elman Neural Networks for time series prediction. The empirical results show that the new hybrid algorithm is a useful tool.

Uncertain natural frequency analysis of composite plates including effect of noise – A polynomial neural network approach

Acknowledgement SN and SS gratefully acknowledge the financial support from Lloyd’s Register Foundation Centre during this work.Peer reviewedPostprin

A Novel Design of Hybrid Polynomial Spline Estimation and GMDH Networks for Modeling and Prediction

GMDH algorithm can well describe the internal structure of objects. In the process of modeling, automatic screening of model structure and variables ensure the convergence rate.This paper studied a novel design of hybrid polynomial spline estimation and GMDH. The polynomial spline function was used to instead of the transfer function of GMDH to characterize the relationship between the input variables and output variables. It has proved that the algorithm has the optimal convergence rate under some conditions. The empirical results show that the algorithm can well forecast tax revenue

Combining group method of data handling models using artificial bee colony algorithm for time series forecasting

Time series forecasting which uses models to predict future values based on some historical data is an important area of forecasting, and has gained the attention of researchers from various related fields of study. In line with its popularity, various models have been introduced for producing accurate time series forecasts. However, to produce an accurate forecast is not an easy feat especially when dealing with nonlinear data due to the abstract nature of the data. In this study, a model for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and Group Method of Data Handling (GMDH) models with variant transfer functions, namely polynomial, sigmoid, radial basis function and tangent was developed. Initially, in this research, the GMDH models were used to forecast the time series data followed by each forecast that was combined using ABC. Then, the ABC produced the weight for each forecast before aggregating the forecasts. To evaluate the performance of the developed GMDH-ABC model, input data on tourism arrivals (Singapore and Indonesia) and airline passengers’ data were processed using the model to produce reliable forecast on the time series data. To validate the evaluation, the performance of the model was compared against benchmark models such as the individual GMDH models, Artificial Neural Network (ANN) model and combined GMDH using simple averaging (GMDH-SA) model. Experimental results showed that the GMDH-ABC model had the highest accuracy compared to the other models, where it managed to reduce the Root Mean Square Error (RMSE) of the conventional GMDH model by 15.78% for Singapore data, 28.2% for Indonesia data and 30.89% for airline data. As a conclusion, these results demonstrated the reliability of the GMDH-ABC model in time series forecasting, and its superiority when compared to the other existing models

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10−12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply

Redes neurais baseadas no método de grupo de manipulação de dados : treinamento, implementações e aplicações

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2013.O Método de Grupo para Manipulação de Dados (GMDH - Group Method of Data Handling) é um modelo de rede neural artificial (RNA) constituído de neurônios compostos por polinômios de baixa ordem. Os neurônios GMDH dispensam o uso das complexas funções de ativação, que demandam grandes esforços dos microprocessadores para que realizem seus cálculos. A simplicidade dos neurônios, em conjunto com outras características, tornam o GMDH uma opção interessante para aplicações de engenharia. Desde a proposta seminal do método, em 1966, diversos trabalhos têm sido desenvolvidos com o intuito de melhorar seus resultados em áreas como mineração de dados, predição, modelagem de sistemas, otimização e reconhecimento de padrões. Este trabalho introduziu uma abordagem teórica e experimental no estudo do GMDH, lidando com vários aspectos de seu processo de treinamento. Como resultado, produziu-se uma ferramenta de desenvolvimento chamada GMDH Box, que permite que o desenvolvedor escolha diferentes parâmetros de treinamento e varições do método, que podem ser aplicados em diferentes áreas. Nesse sentido, três aplicações foram escolhidas: (a) modelagem e predição de sistemas dinâmicos; (b) reconhecimento de padrões em bioinformática e; (c) sistemas embarcados baseados em FPGAs. A identificação de sistemas dinâmicos não lineares é uma tarefa difícil e existem vários relatos na literatura sobre a utilização de diferentes modelos de RNAs para aproximar essa classe de problemas. Este trabalho introduziu a aplicação da modelagem paralela de GMDH nesta área, comparando seus resultados com outros obtidos utilizando-se redes neurais MLP (Multiple Layers Perceptron). Na áarea de bioinformática, o método GMDH foi testado com sete sequências de proteínas cujos tamanhos variam de 14 a 54 resíduos. Os resultados demonstraram que as estruturas terciárias preditas adotam uma forma similar às das estruturas experimentais. Na área de sistemas embarcados, partes do método GMDH foram implementadas em hardware, mais precisamente em FPGAs (Field Programmable Gate Arrays). Um conjunto de experimentos foi realizado em um PC utilizando o FPGA como um coprocessador acessado através do protocolo TCP/IP. O fluxo de projeto demonstrou que é possível implementar-se a rede em hardware, que pode ser utilizada como um acelerador em sistemas embarcados. Experimentos demonstraram que a implementação é efetiva para encontrar-se soluções de boa qualidade para o problema em análise e representam os primeiros resultados da técnica inovadora da aplicação de GMDH em hardware para a solução do problema da predição de estruturas de proteínas. Nas três aplicações, obteve-se resultados interessantes, relacionados ao desempenho e à aplicabilidade do método, comparando-os com resultados obtidos a partir de abordagens mais conhecidas como o modelo MLP. _________________________________________________________________________________ ABSTRACTGMDH (Group Method of Data Handling) are arti cial neural networks (ANNs) composed of neurons constituted of low order polynomials. GMDH neurons are exempt from the use of complex activation functions, which require big e orts from microprocessors to perform their calculations. The simplicity of the GMDH neurons model, along with other characteristics, make GMDH an interesting option for engineering applications. Since the seminal proposal of the GMDH method in 1966, several works have been developed in order to improve its results in di erent areas such as data mining, knowledge discovery, forecasting, systems modeling, optimization and pattern recognition. This work has introduced a theoretical and experimental approach in the study of GMDH, discussing and addressing several aspects of its training process. This work has yielded a development tool called GMDH Box, which allows the designer to choose several GMDH training parameters and variations, which can be applied on different areas. In this direction, three applications have been chosen: (a) modeling and prediction of dynamic systems; (b) pattern recognition in bioinformatics and; (c) embedded systems based on FPGAs. Identi cation and prediction of nonlinear dynamic systems are di cult tasks and there are several reports in the literature about the utilization of dif erent models of ANNs to approximate this class of systems. This work has introduced the application of GMDH parallel modeling to this problem, comparing its results with the ones obtained with MLP (Multiple Layers Perceptron) ANNs. In the bioinformatics area, the GMDH method was tested with seven protein sequences whose sizes vary from 14 to 54 amino acid residues. Results show that the predicted tertiary structures adopt a fold similar to the experimental structures. In the embedded systems area, portions of the GMDH method have been implemented in hardware, more precisely in FPGAs (Field Programmable Gate Arrays). A set of experiments have been performed on a PC using the FPGA as a co-processor accessed through sockets over the TCP/IP protocol. The design ow employed demonstrated that it is possible to implement the network in hardware, which can be used as an accelerator in embedded systems. Experiments show that the proposed implementation is ef ective in finding good quality solutions for the example problem, which represents the early results of the novel technique of applying the GMDH algorithms in hardware for solving the problem of protein structures prediction. In the three applications, this work has detected several interesting results, related to performance and suitability, comparing the results with more well-known approaches such as the MLP model

Modelagem comportamental de amplificadores de potência de radiofrequência baseada no método de grupo de manipulação de dados

Orientador: Prof. Dr. Eduardo Gonçalves de LimaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 28/10/2020Inclui referências: p. 104-108Resumo: Este trabalho aborda a modelagem comportamental de um RFPA não linear, adequado para um DPD de banda base, a ser utilizado na linearização do sistema. O foco principal é a eficácia do algoritmo GMDH para esta tarefa. Trata-se de um modelo de rede polinomial auto-organizado, capaz de se construir e selecionar suas melhores características, a fim de obter um modelo com boa precisão e carga computacional reduzida, o que é interessante na implementação de um DPD em sistemas embarcados e de baixo custo. Além disso, para o RGMDH, é utilizada uma topologia comprovada para redes que processam números reais, para modelar o comportamento complexo de entrada / saída do RFPA. Já para o CGMDH, é proposto um modelo que reduz o número total de coeficientes do modelo, visto que não é necessária uma topologia para representação dos dados complexos na forma real. Ao modelar a função inversa de um RFPA não linear, o modelo baseado no RGMDH reduziu o erro quadrático médio normalizado em pelo menos 7 dB, quando comparado aos modelos de rede neural tradicionais do MLP e usando um número de coeficientes próximo a 150. Ao reduzir o total de coeficientes para próximo de 45, para comparar o RGMDH com o melhor resultado obtido para o CGMDH, o modelo com dados reais obteve um resultado 2,54 dB melhor que o modelo de dados complexos, e 10 dB melhor que o melhor resultado obtido com MLPs de coeficientes reduzidos. Por fim, nota-se que tanto o RGMDH quanto o CGMDH obtiveram uma melhor precisão do que ANNs MLP na modelagem comportamental de RFPAs, com as duas variações do GMDH obtendo desempenho próximas para o RFPA modelado, para uma complexidade computacional similar. Palavras-chave: GMDH, DPD, modelagem, pré-distorção digital, auto-organizadoAbstract: This work addresses the behavioral modeling of a RFPA suitable for a DPD utilized in linearization of the system. The main focus given is in the effectiveness of the GMDH algorithm for this task. This is a self-organized polynomial network model, capable of building itself and selecting its best features, in order to achieve a model with good accuracy and reduced computational burden, which is interesting when implementing the DPD in embedded and low cost systems. In addition to that, a proven topology for networks that process real numbers is utilized, in order to model the complex input/output behavior of the RFPA. When modeling the inverse function of a nonlinear RFPA, the RGMDH based model reduced the normalized mean square error by at least 7 dB, when compared to traditional MLP neural network models and using a number of coefficients close to 150. When reducing the number of coefficients close to 45, in order to compare the RGMDH model with the best result obtained with the CGMDH model, the model processing real data had an NMSE result 2,54 dB lower than the model processing complex data, and 10 dB lower than the best result obtained with the MLP models with reduced coefficients. It can be concluded that both the RGMDH and CGMDH obtained a better performance than the MLP models in the behavioral modeling of a RFPA, with both variations of GMDH having a similar performance for the utilized RFPA dataset, with a similar computational complexity. Keywords: GMDH, DPD, modeling, digital predistortion, self-organize

Cold Active Enzyme Booster Technology (EnBooT) for Biodegradation of P-Xylene

p-xylene is used as a solvent in medical technology, the leather, paint, and rubber industries. The principal pathway of human contact to p-xylene is via soil and groundwater contamination. Bioremediation offers potential advantages such as being cost-effective and environmentally friendly with lesser undue damage to environments. The main aim of this project is to find an enzyme mixture for biodegradation of p-xylene contaminated sites. In this regard, screening of indigenous bacteria, identification of involved enzymes, and biodegradation tests were carried out. The results showed that xylene monooxygenase (XMO) and catechol 2,3-dioxygenase (C2,3D) have a matching end product, they acted in symphony to degrade p-xylene. The mixture of these enzymes confirmed the complete degradation of p-xylene within 48 h in groundwater (initial concentration of 200 mg/L), 7 days in soil tests (initial concentration of 10,000 -12,000 mg/kg of soil) at 15C, which is revolutionary for the industrial sector. In soil column tests, different concentrations of the enzyme mixture were used (1x, 5x, and 10x dilution). In this test, 92-94% p-xylene removal was achieved in the treated soil with a 5x diluted enzyme mixture (contained 10 U/mL of XMO and 20 U/mL of C2,3D). Our results showed that biodegradation is a scale-dependent phenomenon and the maximum degradation rate decreased from ~90% to 68% from the soil column to tank tests. It is due to limited access of enzymes to trapped p-xylene in soil pores, low dissolved oxygen, soil heterogeneity, and free phase contaminant. In addition, one of the major challenges in the practical and commercial application of these enzymes is their inherent instability. Our results showed that immobilization improved the stability of enzymes. For example, micro/nano biochar-chitosan matrices increased the stability of enzymes with more than 50% residual activity after 30 days at 41 C, while the free enzymes had less than 10% of its activity. Overall, this cold-active enzyme mixture can be applied for the biodegradation of all BTEX compounds (benzene, toluene, ethylbenzene, and xylenes). This study could set the guideline for the enzymatic bioremediation of mono-aromatic pollutants in contaminated soil and groundwater under cold conditions

Cold Active Enzyme Booster Technology (EnBooT) for Biodegradation of P-Xylene

p-xylene is used as a solvent in medical technology, the leather, paint, and rubber industries. The principal pathway of human contact to p-xylene is via soil and groundwater contamination. Bioremediation offers potential advantages such as being cost-effective and environmentally friendly with lesser undue damage to environments. The main aim of this project is to find an enzyme mixture for biodegradation of p-xylene contaminated sites. In this regard, screening of indigenous bacteria, identification of involved enzymes, and biodegradation tests were carried out. The results showed that xylene monooxygenase (XMO) and catechol 2,3-dioxygenase (C2,3D) have a matching end product, they acted in symphony to degrade p-xylene. The mixture of these enzymes confirmed the complete degradation of p-xylene within 48 h in groundwater (initial concentration of 200 mg/L), 7 days in soil tests (initial concentration of 10,000 -12,000 mg/kg of soil) at 15°C, which is revolutionary for the industrial sector. In soil column tests, different concentrations of the enzyme mixture were used (1x, 5x, and 10x dilution). In this test, 92-94% p-xylene removal was achieved in the treated soil with a 5x diluted enzyme mixture (contained 10 U/mL of XMO and 20 U/mL of C2,3D). Our results showed that biodegradation is a scale-dependent phenomenon and the maximum degradation rate decreased from ~90% to 68% from the soil column to tank tests. It is due to limited access of enzymes to trapped p-xylene in soil pores, low dissolved oxygen, soil heterogeneity, and free phase contaminant. In addition, one of the major challenges in the practical and commercial application of these enzymes is their inherent instability. Our results showed that immobilization improved the stability of enzymes. For example, micro/nano biochar-chitosan matrices increased the stability of enzymes with more than 50% residual activity after 30 days at 4±1 ºC, while the free enzymes had less than 10% of its activity. Overall, this cold-active enzyme mixture can be applied for the biodegradation of all BTEX compounds (benzene, toluene, ethylbenzene, and xylenes). This study could set the guideline for the enzymatic bioremediation of mono-aromatic pollutants in contaminated soil and groundwater under cold conditions

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications