The design and implementation of a fuzzy logic controller (FLC) are presented, offering a solution to improve the irrigation of rose crops. The objective is to reduce the water consumption and operative costs, taking advantage of intelligent controllers and environmental characteristics in a specific region. Considering that the main controllable variables that affect the growth of plants are relative humidity (RH) and temperature (T), in this study, these variables are used to create a system whose aim is to provide an adequate amount of water for a rose crop in the State of Mexico. The Mamdani method was used for the FLC design and the membership functions, while the area centroid was considered as the defuzzification strategy. After implementing the FLC proposal using a field-programmable gate array (FPGA) in a domestic greenhouse, integrated by an array of [5 × 3] rose plants under natural restrictions, a reduction of 0.2 L per week with respect to the traditional manual irrigation system was found. The proposed design highlights the technological advantages of using a fuzzy logic-controlled irrigation system over traditional methods.
En un sistema en tiempo real no crítico, se emplean utilizando computadoras de placa reducida como son las SBC Raspberry PI por su tamaño compacto y gran desempeño en sus distribuciones de SO Linux. Esto permite utilizar diferentes tipos de sistemas operativos con kernel en tiempo real dependiendo de sus características propias. En este artículo se comparan 2 tipos de kernel utilizando Linux CNC y Raspberry PI OS con PREEMPT RT, utilizando como banco de pruebas la herramienta Cyclictest para la comparativa de latencia y un proceso computacional de complejidad para la comparativa de tiempos de respuesta.
This paper presents a description of application of stochastic weights in a neuron, problem solved through the adaptive estimation achieved with dynamical combination between the identification and estimation; having an adaptive structure that updates the estimated parameters into the integrated filter. The weights are dynamically adjusted in the neuron based on stochastic gradient, affecting the neuronal performance allowing that its response converges to the reference signal. In addition, the error is applied in identification as an innovative gain adjusting the neuron in its inputs and consequently its dendrites signals that are applied into gradient filter adjusting the neuron weights in accordance with the desired signal requirement. Such that the gradient estimation is built based on the Black-box scheme with unknown internal weights. All simulations were developed using Matlab® software.
Within signal processing, parameter estimation is necessary to obtain coefficients applicable to classification, optimization, prediction or identification algorithms. The latter, of interest in this work, refers to the reconstruction of a signal, corrected from the error generated between the desired and the identified signal.
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