PymoNNto: A Flexible Modular Toolbox for Designing Brain-Inspired Neural Networks

The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks. In contrast to other commonly used simulators such as Brian2 and NEST, PymoNNto imposes only minimal restrictions for implementation and execution. The basic structure of PymoNNto consists of one network class with several neuron- and synapse-groups. The behaviour of each group can be flexibly defined by exchangeable modules. The implementation of these modules is up to the user and only limited by Python itself. Behaviours can be implemented in Python, Numpy, Tensorflow, and other libraries to perform computations on CPUs and GPUs. PymoNNto comes with convenient high level behaviour modules, allowing differential equation-based implementations similar to Brian2, and an adaptable modular Graphical User Interface for real-time observation and modification of the simulated network and its parameters.

Prediction of Direct Carbon Emissions of Chinese Provincial Residents under Artificial Neural Networks in Deep Learning Environment

It is aimed to deepen the understanding of the consumption carbon emissions of Chinese provinces, establish an accurate and feasible carbon emission prediction model, develop an urban low-carbon economy, and ensure the sustainable development of Chinese cities. Through the national statistical data information, based on the artificial neural network model, mathematical statistics and deep learning methods are used to learn and analyze the carbon emission data of various provinces in China from 1999 to 2019. The neural network toolbox in Matlab is used to program separately to realize the prediction of carbon emissions by different neural network models. After comparing and analyzing the accuracy and prediction performance, the optimal model for the prediction effect is selected. Finally, based on ArcGIS Engine (Arc Geographic Information Science Engine) and C#.NET platform, the call to Matlab neural network toolbox is realized. The selected model is embedded in the prediction system to complete the development of the entire system. The results show that the carbon emissions of residents in the north are distinctly higher than those in the south. Also, with the passage of time, the rate of carbon emissions continues to accelerate. Compared with other models, Elman neural network has higher accuracy and smaller error in carbon emission prediction. Compared to BP (Back Propagation) neural network, the accuracy is improved by 55.93%, and the prediction performance is improved by 19.48%. The prediction results show that China is expected to reach the peak of carbon emissions from 2027 to 2032. This investigation will provide a theoretical basis to control and plan carbon emissions from Chinese urban residents.

Realization of Linear Neural Network Based on MATLAB Neural Network Toolbox

First of all, this article briefly introduces the structure and algorithm of linear neural network and the characteristics of MATLAB language by reading and combing related literature. Then, the method and basic steps of using MATLAB neural network toolbox to realize linear neural network are given. Based on the neural network toolbox function provided by MATLAB language for network design, users can call related programs according to their own needs, thus eliminating the need to write specific complex and lengthy algorithm details. Finally, a concrete example is given to prove that the method is feasible.

Artificial neural network inspired by quantum computing solutions using the movement model of the PR-02 robot

The paper proposes a new method of quantum computing using control and systems theory as well as matrix-quantum computing. The algorithm developed on the basis of the PR-02 robot’s arm’s movement was implemented in using the Neural Network Toolbox. The application of the neural model instead of the analytic model allowed for obtaining the improvement of the trajectory of the PR-02 robot’s arm movement, while the application of the quantum artificial neural network for the assumed number of quasi-parallel computations equal 1000 did not result in the improvement of the model.

Aplikasi MATLAB untuk Mengenali Karakter Tulisan Tangan

<p>Handwriting recognition is one of the very interesting research object in the field of image processing, artificial intelligence and computer vision. This is due to the handwritten characters is varied in every individual. The style, size and orientation of handwriting characters has made every body’s is different, hence handwriting recognition is a very interesting research object. Handwriting recognition application has been used in quite many applications, such as reading the bank deposits, reading the postal code in letters, and helping peolple in managing documents.</p><p>This paper presents a handwriting recognition application using Matlab. Matlab toolbox that is used in this research are Image Processing and Neural Network Toolbox.</p><p> </p>

Aplicação de técnicas de redes neurais e modelagem atmosférica para elaboração de previsões de vazão na Bacia do Rio Grande (MG)

RESUMO Neste estudo foi proposta a elaboração de um modelo de previsão de vazões no horizonte de dez dias para a Usina Hidrelétrica de Furnas, localizada na Bacia do Rio Grande, Minas Gerais, a partir da aplicação de redes neurais artificiais (RNA), informações de vazão natural e precipitação observada e prevista. O modelo foi desenvolvido utilizando o software Matlab(r) Neural Network Toolbox. Escolheu-se uma rede neural do tipo perceptron multicamadas (MLP), treinada com algoritmo supervisionado de retropropagação Levenberg-Marquardt. As previsões de precipitação foram obtidas a partir do modelo ETA/Centro de Previsão do Tempo e Estudos Climáticos (CPTEC), e utilizadas com e sem tratamento matemático. Foram realizados três experimentos, dividindo-se o histórico de dados em três períodos, sendo o primeiro para a calibração do modelo, o segundo para a validação e o terceiro para os testes. Em cada experimento foi variado o conjunto de dados de entrada, sendo utilizada, no primeiro experimento, somente a vazão passada para prever os dez dias de vazão futura. No segundo foi adicionada a precipitação observada e, no terceiro, a previsão de precipitação. Os resultados da modelagem chuva-vazão obtidos com a previsão de precipitaçãodo modelo ETA não apresentaram melhorias estatísticas em comparação com os experimentos que só utilizaram informações passadas. No entanto, quando se utilizou a previsão de precipitação corrigida matematicamente, observou-se uma melhora sensível tanto nos índices estatísticos quanto na representação da previsão simulada no hidrograma, ficando o desempenho da modelagem proposta neste estudo semelhante à encontrada em modelos conceituais do tipo chuva-vazão.

Desarrollo de un controlador basado en redes neuronales para un sistema multivariable de nivel y caudal

Este artículo presenta el desarrollo de un control neuronal basado en el modelo inverso para el sistema hidráulico multivariable de nivel y caudal de la Universidad Surcolombiana. Con este propósito, se evalúa el grado de acoplamiento entre las variables a controlar a través del método de la matriz de ganancias relativas (RGA), se realiza el modelamiento del sistema y se implementan los controladores en MatLab haciendo uso del Neural Network Toolbox y de Simulink como interfaz de monitoreo y control. El rendimiento del control es evaluado mediante simulaciones y pruebas en tiempo real. La comparación del desempeño del control neuronal frente al control difuso se realiza evaluando tres parámetros: sobreimpulso, error en estado estacionario y tiempo de establecimiento. Los resultados obtenidos demuestran un mejor desempeño frente al controlador fuzzy desarrollado previamente para el mismo sistema. Se evidencia que el error en estado estacionario disminuye notablemente dado que el porcentaje de error máximo es de 0.01% y 0.003% por redes neuronales y de 3% y 1.75% por control difuso, para nivel y caudal respectivamente. En cuanto al sobreimpulso, aunque en el control difuso es mínimo, el control por redes neuronales lo elimina en las dos variables controladas. Para el tiempo de establecimiento se observa que el control neuronal también mejora considerablemente para las dos variables.