An empirical learning-based validation procedure for simulation workflow

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models

Optimal Control of a PEM Fuel Cell for the Inputs Minimization

The trajectory tracking problem of a proton exchange membrane (PEM) fuel cell is considered. To solve this problem, an optimal controller is proposed. The optimal technique has the objective that the system states should reach the desired trajectories while the inputs are minimized. The proposed controller uses the Hamilton-Jacobi-Bellman method where its Riccati equation is considered as an adaptive function. The effectiveness of the proposed technique is verified by two simulations

Adaptive input selection and evolving neural fuzzy networks modeling

This paper suggests an evolving approach to develop neural fuzzy networks for system modeling. The approach uses an incremental learning procedure to simultaneously select the model inputs, to choose the neural network structure, and to update the network weights. Candidate models with larger and smaller number of input variables than the current model are constructed and tested concurrently. The procedure employs a statistical test in each learning step to choose the best model amongst the current and candidate models. Membership functions can be added or deleted to adjust input space granulation and the neural network structure. Granulation and structure adaptation depend of the modeling error. The weights of the neural networks are updated using a gradient-descent algorithm with optimal learning rate. Prediction and nonlinear system identification examples illustrate the usefulness of the approach. Comparisons with state of the art evolving fuzzy modeling alternatives are performed to evaluate performance from the point of view of modeling error. Simulation results show that the evolving adaptive input selection modeling neural network approach achieves as high as, or higher performance than the remaining evolving modeling methods81314CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIG305906/2014-3não temnão te

Industrial time series modelling by means of the neo-fuzzy neuron

Abstract—Industrial process monitoring and modelling represents a critical step in order to achieve the paradigm of Zero Defect Manufacturing. The aim of this paper is to introduce the Neo-Fuzzy Neuron method to be applied in industrial time series modelling. Its open structure and input independency provides fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modelled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the Neo-Fuzzy Neuron is configured and trained according by means of the auxiliary signal, past instants and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modelled. The obtained results indicate the suitability of the Neo-Fuzzy Neuron method for industrial process modelling.Postprint (published version

An IoT Knowledge Reengineering Framework for Semantic Knowledge Analytics for BI-Services

In a progressive business intelligence (BI) environment, IoT knowledge analytics are becoming an increasingly challenging problem because of rapid changes of knowledge context scenarios along with increasing data production scales with business requirements that ultimately transform a working knowledge base into a superseded state. Such a superseded knowledge base lacks adequate knowledge context scenarios, and the semantics, rules, frames, and ontology contents may not meet the latest requirements of contemporary BI-services. Thus, reengineering a superseded knowledge base into a renovated knowledge base system can yield greater business value and is more cost effective and feasible than standardising a new system for the same purpose. Thus, in this work, we propose an IoT knowledge reengineering framework (IKR framework) for implementation in a neurofuzzy system to build, organise, and reuse knowledge to provide BI-services to the things (man, machines, places, and processes) involved in business through the network of IoT objects. The analysis and discussion show that the IKR framework can be well suited to creating improved anticipation in IoT-driven BI-applications

A fast learning algorithm for evolving neo-fuzzy neuron

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulatethe input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments. (C) 2013 Elsevier B. V. All rights reserved.14B194209Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Brazilian Minister of Education and InnovationConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

A fast learning algorithm for evolving neo-fuzzy neuron

This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulatethe input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments14B194209CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIGBrazilian Minister of Education and Innovatio

Contributions to industrial process condition forecasting applied to copper rod manufacturing process

Ensuring reliability and robustness of operation is one of the main concerns in industrial anufacturing processes , dueto the ever-increasing demand for improvements over the cost and quality ofthe processes outcome. In this regard , a deviation from the nominal operating behaviours implies a divergence from the optimal condition specification, anda misalignment from the nominal product quality, causing a critica! loss of potential earnings . lndeed, since a decade ago, the industrial sector has been carried out a significant effortAsegurar la fiabilidad y la robustez es uno de los principales objetivos en la monitorización de los procesos industriales, ya que estos cada vez se encuentran sometidos a demandas de producción más elevadas a la vez que se deben bajar costes de fabricación manteniendo la calidad del producto final. En este sentido, una desviación de la operación del proceso implica una divergencia de los parámetros óptimos preestablecidos, lo que conlleva a una desviación respecto la calidad nominal del producto final, causando así un rechazo de dicho producto y una perdida en costes para la empresa. De hecho, tanto es así, que desde hace más de una década el sector industrial ha dedicado un esfuerzo considerable a la implantación de metodologías de monitorización inteligente. Dichos métodos son capaces extraer información respecto a la condición de las diferentes maquinarias y procesos involucrados en el proceso de fabricación. No obstante, esta información extraída corresponde al estado actual del proceso. Por lo que obtener información respecto a la condición futura de dicho proceso representa una mejora significativa para poder ganar tiempo de respuesta para la detección y corrección de desviaciones en la operación de dicho proceso. Por lo tanto, la combinación del conocimiento futuro del comportamiento del proceso con la consecuente evaluación de la condición del mismo, es un objetivo a cumplir para la definición de las nuevas generaciones de sistemas de monitorización de procesos industriales. En este sentido, la presente tesis tiene como objetivo la propuesta de metodologías para evaluar la condición, actual y futura, de procesos industriales. Dicha metodología debe estimar la condición de forma fiable y con una alta resolución. Por lo tanto, en esta tesis se pretende extraer la información de la condición futura a partir de un modelado, basado en series temporales, de las señales críticas del proceso, para después, en base a enfoques no lineales de preservación de la topología, fusionar dichas señales proyectadas a futuro para conocer la condición. El rendimiento y la bondad de las metodologías propuestas en la tesis han sido validadas mediante su aplicación en un proceso industrial real, concretamente, con datos de una planta de fabricación de alambrón de cobre

Evolving Neo-fuzzy Neural Network With Adaptive Feature Selection

This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical tests and information about the current model performance to decide if a new variable should be added, or if an existing variable should be excluded or kept as an input. The network structure evolves by adding or deleting membership functions and adapting its parameters depending of the input data and modeling error. The performance of the evolving neural fuzzy network with adaptive feature selection is evaluated considering instances of times series forecasting problems. Computational experiments and comparisons show that the proposed approach is competitive and achieves higher or as high performance as alternatives reported in the literature. © 2013 IEEE.341349Kasabov, N., Filev, D., Evolving intelligent systems: Methods, learning, & applications (2006) Proceedings of the International Symposium on Evolving Fuzzy Systems, pp. 8-18. , septLemos, A., Gomide, F., Caminhas, W., Multivariable gaussian evolving fuzzy modeling system IEEE Transactions on Fuzzy Systems, 1 (2011), pp. 91-104Lughofer, E., On-line incremental feature weighting in evolving fuzzy classifiers (2011) Fuzzy Sets Systems, 163 (1), pp. 1-23Li., Y., On incremental and robust subspace learning (2004) Pattern Recognition, 37, pp. 1509-1518Katakis, I., Tsoumakas, G., Vlahavas, I., Dynamic feature space and incremental feature selection for the classification of textual data streams (2006) Proc. Int. Workshop on Knowledge Discovery from Data Streams, pp. 107-116. , SpringerLemos, A., Caminhas, W., Gomide, F., Evolving fuzzy linear regression trees with feature selection (2001) Proc. of the IEEE Workshop on Evolving and Adaptive Intelligent Systems, 1, pp. 31-38Silva, A.M., Caminhas, W.M., Lemos, A.P., Gomide, F., Evolving neural fuzzy network with adaptive feature selection (2012) Machine Learning and Applications (ICMLA), pp. 440-445. , 11th International Conference on 2, dec 2012Silva, A.M., Caminhas, W.M., Lemos, A., Gomide, F., A fast learning algorithm for evolving neo-fuzzy neuron (2013) Applied Soft Computing, 0. , http://www.sciencedirect.com/science/article/pii/S1568494613001373, Online Available:Yamakawa, T., Uchino, E., Miki, T., Kusabagi, H., A neo fuzzy neuron and its applications to system identification and predictions to system behavior (1992) Proc. of the Int. Conf. on Fuzzy Logic and Neural Networks, 1, pp. 477-484Caminhas, W., Gomide, F., A fast learning algorithm for neofuzzy networks (2000) Proc. Information Processing and Management of Uncertainty in Knowledge Based Systems, 1 (1), pp. 1784-1790Bazaraa, M., Sherali, H., Shetty, C., (1993) Nonlinear Programming: Theory and Algorithms, , 3rd ed. John Wiley & SonsAllen, M., (1997) Understanding Regression Analysis, , 1st ed. Springer Ed. SpringerPotts, D., Sammut, C., Incremental learning of linear model trees (2004) Machine Learning, 61 (1), pp. 5-48Wang, D., Zeng, X., Keane, J., A structure evolving learning method for fuzzy systems Evolving Systems, 1 (2010), pp. 83-95Angelov, P., Filev, D., Simplets: A simplified method for learning evolving takagi-sugeno fuzzy models (2005) Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1068-1073. , FUZZ-IEEE ?05Lughofer, E., Angelov, P., Handling drifts and shifts in on-line data streams with evolving fuzzy systems (2011) Applied Soft Computing, 11 (2), pp. 2057-2068. , marKasabov, N., Song, Q., Denfis: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction (2002) IEEE Transactions on Fuzzy Systems, 10 (2), pp. 144-154Angelov, P., Filev, D., An approach to online identification of takagi-sugeno fuzzy models (2004) IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, 34 (1), pp. 484-498Angelov, P., Zhou, X., Evolving fuzzy systems from data streams in real-Time (2006) Proc. of the Int. Symposium on Evolving Fuzzy Systems, pp. 29-35Jamsa, K., Klander, L., (1997) Jamsa?s C/C++ Programmer?s Bible: The Ultimate Guide to C/C++ Programming, , 2nd ed. PearsonMackey, M., Glass, L., Oscillation and chaos in physiological control systems (1977) Science, 197 (4300), pp. 287-289. , julyLeite, D., Ballini, R., Costa, P., Gomide, F., Evolving fuzzy granular modeling from nonstationary fuzzy data streams Evolving Systems, 3 (2012), pp. 65-7