A Look-Up Table Based Fractional Order Composite Controller Synthesis Method for the PMSM Speed Servo System

Considering the performance requirements in actual applications, a look-up table based fractional order composite control scheme for the permanent magnet synchronous motor speed servo system is proposed. Firstly, an extended state observer based compensation scheme was adopted to suppress the motor parametric uncertainties and convert the speed servo plant into a double-integrator model. Then, a fractional order proportional-derivative (PDμ) controller was adopted as the speed controller to provide the optimal step response performance for the servo system. A universal look-up table was established to estimate the derivative order of the PDμ controller, according to the optimal samples collected by an improved differential evolution algorithm. With the look-up table, the optimal PDμ controller can be tuned analytically. Simulation and experimental results show that the servo system using the composite control scheme can achieve optimal tracking performance and has robustness to the motor parametric uncertainties and disturbance torques.

A Novel Enhanced Differential Evolution Algorithm for Outbound Logistics of the Poultry Industry in Thailand

Outbound logistics is a crucial field of logistics management. This study considers a planning distribution for the poultry industry in Thailand. The goal of the study is to minimize the transportation cost for the multi-depot vehicle-routing problem (MDVRP). A novel enhanced differential evolution algorithm (RI-DE) is developed based on a new re-initialization mutation formula and a local search function. A mixed-integer programming formulation is presented in order to measure the performance of a heuristic with GA, PSO, and DE for small-sized instances. For large-sized instances, RI-DE is compared to the traditional DE algorithm for solving the MDVRP using published benchmark instances. The results demonstrate that RI-DE obtained a near-optimal solution of 99.03% and outperformed the traditional DE algorithm with a 2.53% relative improvement, not only in terms of solution performance, but also in terms of computational time.

Artificial Intelligence Education System Based on Differential Evolution Algorithm to Optimize SVM

The artificial intelligence education system promotes the rooting of artificial intelligence in the education field and accelerates its entry into the era of intelligent education. This article focuses on the development of the artificial intelligence education system and proposes an artificial intelligence education system based on differential evolution algorithm optimization support vector machine. First, the processing of educational demand information data is automated, then a differential evolution algorithm is built to optimize the support vector machine model, and the model is used to implement various educational tasks to achieve automated education. The test results show that the model classification accuracy, classification recall rate, classification accuracy rate, and F1-score value are 4 items. Performances have been improved to improve the efficiency of education work and provide a reference for exploring the application and practice of artificial intelligence in education.

Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation

Site-specific spatially continuous soil texture data is required for many purposes such as the simulation of carbon dynamics, the estimation of drought impact on agriculture, or the modeling of water erosion rates. At large scales, there are often only conventional polygon-based soil texture maps, which are hardly reproducible, contain abrupt changes at polygon borders, and therefore are not suitable for most quantitative applications. Digital soil mapping methods can provide the required soil texture information in form of reproducible site-specific predictions with associated uncertainties. Machine learning models were trained in a nested cross-validation approach to predict the spatial distribution of the topsoil (0–30 cm) clay, silt, and sand contents in 100 m resolution. The differential evolution algorithm was applied to optimize the model parameters. High-quality nation-wide soil texture data of 2,991 soil profiles was obtained from the first German agricultural soil inventory. We tested an iterative approach by training models on predictor datasets of increasing size, which contained up to 50 variables. The best results were achieved when training the models on the complete predictor dataset. They explained about 59% of the variance in clay, 75% of the variance in silt, and 77% of the variance in sand content. The RMSE values ranged between approximately 8.2 wt.% (clay), 11.8 wt.% (silt), and 15.0 wt.% (sand). Due to their high performance, models were able to predict the spatial texture distribution. They captured the high importance of the soil forming factors parent material and relief. Our results demonstrate the high predictive power of machine learning in predicting soil texture at large scales. The iterative approach enhanced model interpretability. It revealed that the incorporated soil maps partly substituted the relief and parent material predictors. Overall, the spatially continuous soil texture predictions provide valuable input for many quantitative applications on agricultural topsoils in Germany.

Logistic Map and Exponential Scaling Factor based Differential Evolution

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.

Integration of the differential evolution algorithm and the Monte Carlo code to create a spectrometric detector model

A standard procedure for characterizing the high-purity germanium detector (HPGe), manufactured by Canberra Industries Inc [1], is performed directly by the company using patented methods. However, the procedure is usually expensive and must be repeated because the characteristics of the HPGe crystal change over time. In this work, the principles of a technique are developed for use in obtaining and optimizing the detector characteristics based on a cost-effective procedure in a standard research laboratory. The technique requires that the detector geometric parameters are determined with maximum accuracy by the Monte Carlo method [2] in parallel with the optimization based on evolutionary algorithms. The development of this approach facilitates modeling of the HPGe detector as a standardized procedure. The results will be also beneficial in the development of gamma spectrometers and/or their calibrations before routine measurements.