52- #1161 DISEÑO Y DESARROLLO DE UNA RED NEURONAL TIPO PERCEPTRÓN SIMPLE EN EXCEL PARA LA CLASIFICACIÓN Y SELECCIÓN DE PROVEEDORES EN LAS TIENDAS DE CONVENIENCIA DE YURIRIA, GUANAJUATO.

Este artículo tiene como propósito el diseño y desarrollode una red neuronal tipo perceptrón simple en Excelpara la clasificación y selección de proveedores deacuerdo con los atributos establecidos para las tiendasde conveniencia en la industria del comercio minoristade la región de Yuriria, Guanajuato. La metodologíautilizada para el desarrollo de dicha red neuronal constade 10 etapas: 1) Definir los atributos pertinentes comovariables de entrada para evaluar las tiendas deconveniencia, 2) Seleccionar los proveedores a serevaluados, 3) Conceptualizar la variable de respuestaen forma binaria, 4) Diseñar la arquitectura de la redneuronal tipo perceptrón simple, 5) Establecer lospatrones de entrenamiento en base al número deproveedores, 6) Entrenar la red con el algoritmo Delta,7) Validar la red con nuevos proveedores, 8) Probar lared neuronal con un nuevo proveedor, 9) Analizar laclasificación obtenida, y por último, 10) Priorizar losproveedores en base a los resultados de la red. Laarquitectura utilizada es tipo 5-1. Se utilizaron 7patrones en la etapa de entrenamiento, 2 en la etapade validación y 1 en la etapa de prueba. Se obtuvieronporcentajes de eficiencia del 100% en las tres etapas.El diseño propuesto de clasificación puede serreplicado en cualquier sector productivo o de servicios,para pequeñas y grandes empresas

Stochastic plans in SMEs: A novel multidimensional fuzzy logic system (mFLS) approach

Manufacturing planning in small and medium enterprises (SMEs) uses a deterministic behavior, and the execution of these plans has a stochastic behavior. The evaluation of the manufacturing planning is based on a simple criterion as job on time or job delayed, without integrating conditions of uncertainty in the cycle times for each job. The aim of this paper is to propose a novel multidimensional stochastic Fuzzy Logic System (msFLS) approach to execute a plan with stochastic behavior in knitting SMEs and their evaluation. In this paper, two main contributions are identified. On one hand, the generation of a multi-dimensional diffuse system is proposed. Normal probability density function is used to generate multi linguistic variables to transform deterministic plans to stochastic plans in knitting SMEs. The fuzzy subsets or linguistic terms are labelled and categorized in a simple and clear language as poor (P), regular (R), good (G) and excellent (E). The Gaussian function was used as a membership function. On the other hand, the second contribution is the use of the sum of frequencies in the stage of implication for the multi-Fuzzy system. This research was validated through an integration of two different intelligent techniques such as the proposed novel msFLS and artificial neural networks. Neural networks were used as a generalization mechanism to perform any stochastic planning in the knitting companies. The inputs and outputs of the fuzzy system are used as training patterns in the neural network. The stages of the proposed approach are explicitly described and applied to random data and validated with real data of SMEs of the South of Guanajuato, Mexico. The proposed system had a positive response in the textile company, which continues to be used to carry out its manufacturing planning and the evaluation of its execution

Stochastic plans in SMEs: A novel multidimensional fuzzy logic system (mFLS) approach

Manufacturing planning in small and medium enterprises (SMEs) uses a deterministic behavior, and the execution of these plans has a stochastic behavior. The evaluation of the manufacturing planning is based on a simple criterion as job on time or job delayed, without integrating conditions of uncertainty in the cycle times for each job. The aim of this paper is to propose a novel multidimensional stochastic Fuzzy Logic System (msFLS) approach to execute a plan with stochastic behavior in knitting SMEs and their evaluation. In this paper, two main contributions are identified. On one hand, the generation of a multi-dimensional diffuse system is proposed. Normal probability density function is used to generate multi linguistic variables to transform deterministic plans to stochastic plans in knitting SMEs. The fuzzy subsets or linguistic terms are labelled and categorized in a simple and clear language as poor (P), regular (R), good (G) and excellent (E). The Gaussian function was used as a membership function. On the other hand, the second contribution is the use of the sum of frequencies in the stage of implication for the multi-Fuzzy system. This research was validated through an integration of two different intelligent techniques such as the proposed novel msFLS and artificial neural networks. Neural networks were used as a generalization mechanism to perform any stochastic planning in the knitting companies. The inputs and outputs of the fuzzy system are used as training patterns in the neural network. The stages of the proposed approach are explicitly described and applied to random data and validated with real data of SMEs of the South of Guanajuato, Mexico. The proposed system had a positive response in the textile company, which continues to be used to carry out its manufacturing planning and the evaluation of its execution.La planeación de la manufactura en pequeñas y medianas empresas (PYMES) utiliza un comportamiento determinista, y la ejecución de estos planes tiene un comportamiento estocástico. La evaluación de la planeación de manufactura se basa en un criterio simple como trabajo a tiempo o trabajo retrasado, sin integrar condiciones de incertidumbre en los tiempos de ciclo para cada trabajo. En este artículo se propone un enfoque nuevo denominado sistema estocástico multidimensional de lógica difusa (msFLS) para realizar un plan con comportamiento estocástico en las pymes de tejido de punto. Esta investigación plantea dos contribuciones principales: La primera es la generación de un sistema difuso multidimensional. La función de densidad de probabilidad normal se utiliza para generar variables multi-lingüísticas como función de transformación del plan determinístico a un plan estocástico en las pymes de tejido de punto. Los subconjuntos difusos o los términos lingüísticos se etiquetan y categorizan en un claro y simple lenguaje como: pobres (P), regulares (R), buenos (G) y excelentes (E). La función gaussiana fue utilizada como función de membresía. El segundo es el uso del indicador “suma de frecuencias” en la etapa de implicación para el sistema multi-difuso. Esta investigación fue validada a través de la integración de dos técnicas inteligentes diferentes: msFLS y redes neuronales. Las redes neuronales se utilizaron como un mecanismo de generalización para realizar cualquier planeación estocástica en las empresas de tejido de punto. Las entradas y salidas del sistema difuso se utilizan como patrones de entrenamiento en la red neuronal. Las etapas del enfoque propuesto se describen explícitamente y se aplican a datos aleatorios múltiples y se validan con datos reales de PYMES del Sur de Guanajuato, México. El sistema propuesto tuvo una respuesta positiva en la empresa textil, el cual sigue utilizándose para realizar sus planeaciones de trabajos y la evaluación de su ejecución

Stochastic plans in SMEs: A novel multidimensional fuzzy logic system (mFLS) approach

Manufacturing planning in small and medium enterprises (SMEs) uses a deterministic behavior, and the execution of these plans has a stochastic behavior. The evaluation of the manufacturing planning is based on a simple criterion as job on time or job delayed, without integrating conditions of uncertainty in the cycle times for each job. The aim of this paper is to propose a novel multidimensional stochastic Fuzzy Logic System (msFLS) approach to execute a plan with stochastic behavior in knitting SMEs and their evaluation. In this paper, two main contributions are identified. On one hand, the generation of a multi-dimensional diffuse system is proposed. Normal probability density function is used to generate multi linguistic variables to transform deterministic plans to stochastic plans in knitting SMEs. The fuzzy subsets or linguistic terms are labelled and categorized in a simple and clear language as poor (P), regular (R), good (G) and excellent (E). The Gaussian function was used as a membership function. On the other hand, the second contribution is the use of the sum of frequencies in the stage of implication for the multi-Fuzzy system. This research was validated through an integration of two different intelligent techniques such as the proposed novel msFLS and artificial neural networks. Neural networks were used as a generalization mechanism to perform any stochastic planning in the knitting companies. The inputs and outputs of the fuzzy system are used as training patterns in the neural network. The stages of the proposed approach are explicitly described and applied to random data and validated with real data of SMEs of the South of Guanajuato, Mexico. The proposed system had a positive response in the textile company, which continues to be used to carry out its manufacturing planning and the evaluation of its execution

INTELLIGENT NEURO-FUZZY FABRIC EVALUATION SYSTEM: A NOVEL MULTI-DIMENSIONAL STOCHASTIC FUZZY SYSTEM AND A GENERATOR OF TRAINING PATTERNS FOR AN ARTIFICIAL NEURAL NETWORK

The aim of this paper is to develop a novel multidimensional stochastic Fuzzy Logic System (msFLS) based on normal probability density function to generate multi training patterns of each quality characteristic and used by a neural network. The approach proposed is comprised of three modules. In the first module, a novel multi-dimensional fuzzy system is developed. This approach uses gaussian membership function. Four linguistic labels are used. The fuzzy operation, implication and aggregation method are applied. The fuzzify linguistic outputs obtained are used as target vector T by the second module at two-layer feed-forward backpropagation neural network with a two-element input, ten hidden tansig neurons, and four purelin output neuron used to evaluate and classified each quantitive characteristic. The third module is the validation of the textile quality for multiple goods where the values are defuzzify in a range of 1-10 and classified as linguistic label correspondent. Validation was performed in a knitted textile company in the South of Guanajuato

Design and Development of an Optimal Control Model in System Dynamics through State-Space Representation

Control engineering and state-space representation are valuable tools in the analysis and design of dynamic systems. In this research, a methodology is proposed that uses these approaches to transform a system-dynamics simulation model into a mathematical model. This is achieved by expressing input, output and state variables as input, output and state vectors, respectively, allowing the representation of the model in matrix form. The resulting model is linear and time-invariant, facilitating its analysis and design. Through the use of this methodology, the system transfer matrix is obtained, which allows the analysis and design of the optimal control of the simulation model. The Ackermann gain-control technique is used to determine the optimal control of the system, which results in a shorter settlement time. This research proposal seeks to mathematically strengthen simulation models and provide an analytical alternative through modern control engineering in SD simulation models. This would allow more informed and effective decisions in the implementation of dynamic systems

Design and Development of a Mathematical Model for an Industrial Process, in a System Dynamics Environment

This research proposes a methodology based on control engineering, transforming the simulation model of system dynamics into a mathematical model expressed as a system transfer function. The differential equations of a time domain present in the Forrester diagram are transformed into a frequency domain based on the Laplace transform. The conventional control engineering technique is used to present and reduce the dynamic system in a block diagram as a mechanism for determining the structure of the system. The direct path equation and the feedback equation are determined to obtain mathematical models that explain the trajectory of the behavior of each state variable through a transfer function in response to the different inputs of the system. The research proposal is based on presenting an alternative of analytical validation for more robust decision-making to systems dynamics models, based on the explanation of the system structure through a transfer function and its analysis of stability and external controllability for the system dynamics model under study. The results are visually analyzed in a root diagram

Tailored Algorithm for Sensitivity Enhancement of Gas Concentration Sensors Based on Tunable Laser Absorption Spectroscopy

In this work, a novel tailored algorithm to enhance the overall sensitivity of gas concentration sensors based on the Direct Absorption Tunable Laser Absorption Spectroscopy (DA-ATLAS) method is presented. By using this algorithm, the sensor sensitivity can be custom-designed to be quasi constant over a much larger dynamic range compared with that obtained by typical methods based on a single statistics feature of the sensor signal output (peak amplitude, area under the curve, mean or RMS). Additionally, it is shown that with our algorithm, an optimal function can be tailored to get a quasi linear relationship between the concentration and some specific statistics features over a wider dynamic range. In order to test the viability of our algorithm, a basic C 2 H 2 sensor based on DA-ATLAS was implemented, and its experimental measurements support the simulated results provided by our algorithm

EVOLUTION OF 2K FACTORIAL DESIGN: EXPANSION AND CONTRACTION OF THE EXPERIMENTAL REGION WITH A FOCUS ON FUZZY LEVELS

This research presents a novel approach of an evolution of the 2k factorial design (E2kFD) which is based on a fuzzy search heuristic technique. Moreover, this approach allows multiple horizontal and vertical exploration spaces, expanding and contracting the experimental region within the region of operability. It can be achieved by defining a variable position for each point or treatment of study factors in its geometric representation, as an alternative to the conventional 2k factorial design in which fixed positions at the vertices of its geometric representations are defined. In general, the main disadvantage of model-based DoE methods is the requirement for fast and reliable decision making in setting the deterministic value for each level. It is often that the value of the levels is unknown in advance inducing imprecision and vagueness when these are determined by experts based on their heuristic knowledge. For this reason, the proposed method combines the main advantages to use values with uncertainty for each one of the levels and to be able to explore inside and outside of the experimental region to assign variables positions to coded levels, and therefore the method works iteratively. Moreover, in the proposed fuzzy search heuristic technique approach the high and low levels of each factor are considered as linguistic variables. These are classified into three linguistic labels in a simple and clear language as: regular, major and minor which are used as an indicator of strength. A maximum fuzzy operator in the implication and aggregation stages are used to search the highest membership value and their position to assign feasible fuzzy levels as one of the scientific contributions of the present investigation. The method is demonstrated with a simulation, which shows the potential of the proposed approach. Additionally, the traditional 2k factorial and our evolved E2kFD expert designs were validated conducting the experimental tests in a textile company in southern Guanajuato, Mexico. Finally, by comparing the results between the traditional and our proposed designs, it will be shown that better explanation and prediction models for the response variables under study are obtained with the E2kFD proposed design