Tensor-Based Recursive Least-Squares Adaptive Algorithms with Low-Complexity and High Robustness Features

The recently proposed tensor-based recursive least-squares dichotomous coordinate descent algorithm, namely RLS-DCD-T, was designed for the identification of multilinear forms. In this context, a high-dimensional system identification problem can be efficiently addressed (gaining in terms of both performance and complexity), based on tensor decomposition and modeling. In this paper, following the framework of the RLS-DCD-T, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming to attenuate the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments.

The Age–Period–Cohort Problem in Hedonic House Prices Models

The age–period–cohort problem has been studied for decades but without resolution. There have been many suggested solutions to make the three effects estimable, but these solutions mostly exploit non-linear specifications. Yet, these approaches may suffer from misspecification or omitted variable bias. This paper is a practical-oriented study with an aim to empirically disentangle age–period–cohort effects by providing external information on the actual depreciation of housing structure rather than taking age as a proxy. It is based on appraisals of the improvement values of properties in New Zealand to estimate the age-depreciation effect. This research method provides a novel means of solving the identification problem of the age, period, and cohort trilemma. Based on about half a million housing transactions from 1990 to 2019 in the Auckland Region of New Zealand, the results show that traditional hedonic prices models using age and time dummy variables can result, ceteris paribus, in unreasonable positive depreciation rates. The use of the improvement values model can help improve the accuracy of home value assessment and reduce estimation biases. This method also has important practical implications for property valuations.

Simulation of the electroerosion treatment process using neural networks

The paper provides formalization and construction of a model of the process of electrical discharge machining. When describing the process, a T-shaped equivalent circuit containing an RLC circuit was used. Determine the transfer function of the proposed substitution scheme. Also, a task is formulated and an algorithm for neural network parametric identification of a T-shaped equivalent circuit is proposed. The problem is posed and an algorithm is developed for neural network parametric identification of the equivalent circuit with a computational experiment, the formation of training samples on its basis, and the subsequent training of dynamic and static neural networks used in the identification problem. The process was simulated in Simulink, Matlab package. Acceptable coincidence of the calculated data with the experimental ones showed that the proposed model of electrical discharge machining reflects real electromagnetic processes occurring in the interelectrode gap.

Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.

Application of Virtualization for Process Control Experiments

The virtualization of the Festo process control teaching platform and the implementation process of extending it with real industry applications are introduced. Taking the heating process of the water tank as an example, the model extraction method of the real object is analyzed in detail, and the model identification problem of the low-order linear control object is solved. Through the introduction of the creation process of the object model on the virtual platform, a feasible way is pointed out for similar applications. On this basis, it is proposed to integrate the teaching platform with the specific industrial industry in the virtualized environment, broaden the breadth of process control teaching, and point out new ideas for building a teaching profession with industry support.

Hierarchical Quasi-Fractional Gradient Descent Method for Parameter Estimation of Nonlinear ARX Systems Using Key Term Separation Principle

Recently, a quasi-fractional order gradient descent (QFGD) algorithm was proposed and successfully applied to solve system identification problem. The QFGD suffers from the overparameterization problem and results in estimating the redundant parameters instead of identifying only the actual parameters of the system. This study develops a novel hierarchical QFDS (HQFGD) algorithm by introducing the concepts of hierarchical identification principle and key term separation idea. The proposed HQFGD is effectively applied to solve the parameter estimation problem of input nonlinear autoregressive with exogeneous noise (INARX) system. A detailed investigation about the performance of HQFGD is conducted under different disturbance conditions considering different fractional orders and learning rate variations. The simulation results validate the better performance of the HQFGD over the standard counterpart in terms of estimation accuracy, convergence speed and robustness.

Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification

Most real-time systems are nonlinear in nature, and their optimization is very difficult due to inherit stiffness and complex system representation. The computational intelligent algorithms of evolutionary computing paradigm (ECP) effectively solve various complex, nonlinear optimization problems. The differential evolution algorithm (DEA) is one of the most important approaches in ECP, which outperforms other standard approaches in terms of accuracy and convergence performance. In this study, a novel application of a recently proposed variant of DEA, the so-called, maximum-likelihood-based, adaptive, differential evolution algorithm (ADEA), is investigated for the identification of nonlinear Hammerstein output error (HOE) systems that are widely used to model different nonlinear processes of engineering and applied sciences. The performance of the ADEA is evaluated by taking polynomial- and sigmoidal-type nonlinearities in two case studies of HOE systems. Moreover, the robustness of the proposed scheme is examined for different noise levels. Reliability and consistent accuracy are assessed through multiple independent trials of the scheme. The convergence, accuracy, robustness and reliability of the ADEA are carefully examined for HOE identification in comparison with the standard counterpart of the DEA. The ADEA achieves the fitness values of 1.43 × 10−8 and 3.46 × 10−9 for a population size of 80 and 100, respectively, in the HOE system identification problem of case study 1 for a 0.01 nose level, while the respective fitness values in the case of DEA are 1.43 × 10−6 and 3.46 × 10−7. The ADEA is more statistically consistent but less complex when compared to the DEA due to the extra operations involved in introducing the adaptiveness during the mutation and crossover. The current study may consider the approach of effective nonlinear system identification as a step further in developing ECP-based computational intelligence.

PELATIHAN PEMBUATAN MEDIA PEMBELAJARAN MENGGUNAKAN POWERPOINT SCREEN RECORDING BAGI GURU DI SMP KESATRIAN 2 SEMARANG

ABSTRAKPandemi covid 19 memang membawa dampak yang sangat besar bagi dunia dan bagi bangsa Indonesia pada khususnya. Dunia pendidikan yang biasanya bisa bertemu tatap muka antara siswa dan guru atauu mahasiswa dengan dosen, karena pandemi maka pembelajaran dilaksanakan secara online atau pembelajaran jarak jauh. Tantangan yang dihadapi oleh para pendidik di era pandemi ini adalah bagaimana harus merevisi dan merubah media pembelajaran dan strategi pembelajaran yang harus dibuat supaya siswa bisa memahami materi yang diberikan. Powerpoint merupakan salah satu bagian dari software Microsoft office yang biasa digunakan untuk membuat presentasi ataupun media pembelajaran. Tujuan dari kegiatan ini untuk meningkatkan pengetahuan dan pelatihan tentang pemanfaatan powerpoint screen recording. Metode pelaksanaan pengabdian ini yaitu identifikasi permasalahan, pendekatan penyelesaian masalah dan hasilnya 40 peserta akan menggunakan manfaat dari pelatihan untuk proses belajar mengajar di sekolah, dan 42 peserta menyatkan bahwa pelatihan tersebut sangat bermanfaat dalam proses kegiatan pembelajaran. Kata kunci: powerpoint screen recording; PJJ ABSTRACTThe COVID-19 pandemic has had a huge impact on the world and the Indonesian people in particular. The world of education is usually able to meet face-to-face between students and teachers or students and lecturers, because of the pandemic, learning is carried out online or distance learning. The challenge faced by educators in this pandemic era is how to revise and change learning media and learning strategies that must be made so that students can understand the material provided. Powerpoint is one part of Microsoft office software which is commonly used to make presentations or learning media. The purpose of this activity is to increase knowledge and training on the use of powerpoint screen recording. The method of implementing this service is problem identification, problem solving approach and the result is that 40 participants will use the benefits of the training for the teaching and learning process in schools, and 42 participants stated that the training was very useful in the process of learning activities. Keywords: powerpoint screen recording; PJJ

Identification and Control of Multidimensional Delayed Processes in Conditions of Incomplete Information

Problems of identification and control of multidimensional discrete-continuous processes with delay in conditions of incomplete information about the object are considered. In such conditions, the form of parametric equations for various channels of the object is absent due to the lack of a priori information. Moreover, multidimensional processes have stochastic dependences of the components of the vector of output variables. Under such conditions, the mathematical description of such processes leads to a system of implicit equations. Nonparametric identification and control algorithms for multidimensional systems are proposed. The main task of modeling such processes is to determine the predicted values of the output variables from the known input. Moreover, for implicit equations, it is only known that one or another output variable can depend on other input and output variables that determine the state of a multidimensional system. In this study, a nontrivial situation arises when solving a system of implicit equations under conditions when the dependences between the components of the output variables are unknown. The application of the parametric theory of identification in this case will not lead to success. One of the possible directions is the use of the theory of nonparametric systems. The main content of the work is the solution of the identification problem in the presence of dependencies of the output variables and then the solution of the control problem for such a process. Here you should pay attention to the fact that when determining the reference actions for a multidimensional system, it is first necessary to solve the system of reference actions, since it is not possible to choose arbitrarily setting influences from the range of definition of output variables. Computational eXperiments aimed at investigating the effectiveness of the proposed identification and control algorithms are presented.