An iterative algorithm for simulation error based identification of polynomial input–output models using multi-step prediction

Effective identification of polynomial input–output models for applications requiring long-range prediction or simulation performance relies on both careful model selection and accurate parameter estimation. The simulation error minimisation (SEM) approach has been shown to provide significant advantages in the model selection phase by ruling out candidate models with good short-term prediction capabilities but unsuitable long-term dynamics. However, SEM-based parameter estimation has been generally avoided due to excessive computational effort. This article extends to the nonlinear case a computationally efficient approach for this task, that was previously developed for linear models, based on the iterative estimation of predictors with increasing prediction horizon. Conditions for the applicability of the approach to various model classes are also discussed. Finally, some examples are provided to show the effectiveness and computational convenience of the proposed algorithm for polynomial input–output identification, as well as the improvements achievable by enforcing SEM parameter estimation. A benchmark for nonlinear identification is also analysed, with encouraging results.     

Time–frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification

In this paper, we design time–frequency localized three-band biorthogonal linear phase wavelet filter bank for epileptic seizure electroencephalograph (EEG) signal classification. Time–frequency localized analysis and synthesis low-pass filters (LPF) are designed using convex semidefinite programming (SDP) by transforming a nonconvex problem into a convex SDP using semidefinite relaxation technique. Three-band parameterized lattice biorthogonal linear phase perfect reconstruction filter bank (BOLPPRFB) is chosen and nonlinear least squares algorithm is used to determine its parameters values that generate the designed analysis and synthesis LPF such that the band-pass and high-pass filters are also well localized in time and frequency domain. The designed analysis and synthesis three-band wavelet filter banks are compared with the standard two-band filter banks like Daubechies maximally regular filter banks, Cohen–Daubechies–Feauveau (CDF) biorthogonal filter banks and orthogonal time–frequency localized filter banks. Kruskal–Wallis statistical test is employed to measure the statistical significance of the subband features obtained from the various two and three-band filter banks for epileptic seizure EEG signal classification. The results show that the designed three-band analysis and synthesis filter banks both outperform two-band filter banks in the classification of seizure and seizure-free EEG signals. The designed three-band filter banks and multi-layer perceptron neural network (MLPNN) are further used together to implement a signal classifier that provides classification accuracy better than the recently reported results for epileptic seizure EEG signal classification.     

Optimization of oval–round pass design using genetic algorithm

The primary purpose of this paper is to propose a computer aided optimal design system to support a generalized oval–round pass design, which is widely used as both intermediate and final passes in the process of rod rolling. This system, which is based on a hybrid model and the genetic algorithm, is developed to improve the efficiency, to reduce the manufacturing errors, as well as to extend the useful life of rolls through uniform wear design. Generalized parametric equations are established for geometrical modeling, graphic plotting of oval–round passes, as well as calculation of the cross section area, contact area and the lengths of contact arcs along the cross section of round groove in the MATLAB programming environment. Moreover, these equations can also realize the parametric transformation between roll profile and mathematical models for the oval–round pass design and optimization. The genetic algorithm is employed for the optimal design of oval–round passes in this paper. The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs. To reduce the complexity and computational burden of the system, some reliable empirical formulas for the calculations of contact area and contact arc length are applied. Finally, the proposed approach is applied to an oval–round pass design. Through simulation and comparison of results against experimental data acquired from literature, it is found that this system is reliable, effective and easier to use.     

Assessment of composite beam performance using GWO–ELM metaheuristic algorithm

Composite beams (CBs) include concrete slabs jointed to the steel parts by the shear connectors, which highly popular in modern structures such as high rise buildings and bridges. This study has investigated the structural behavior of simply supported CBs in which a concrete slab is jointed to a steel beam by headed stud shear connector. Determining the behavior of CB through empirical study except its costly process can also lead to inaccurate results. In this case, AI models as metaheuristic algorithms could be effectively used for solving difficult optimization problems, such as Genetic algorithm, Differential evolution, Firefly algorithm, Cuckoo search algorithm, etc. This research has used hybrid Extreme machine learning (ELM)–Grey wolf optimizer (GWO) to determine the general behavior of CB. Two models (ELM and GWO) and a hybrid algorithm (GWO–ELM) were developed and the results were compared through the regression parameters of determination coefficient (R²) and root mean square (RMSE). In testing phase, GWO with the RMSE value of 2.5057 and R² value of 1.2510, ELM with the RMSE value of 4.52 and R² value of 1.927, and GWO–ELM with the RMSE value of 0.9340 and R² value of 0.9504 have demonstrated that the hybrid of GWO–ELM could indicate better performance compared to solo ELM and GWO models. In this case, GWO–ELM could determine the general behavior of CB faster, more accurate and with the least error percentages, so the hybrid of GWO–ELM is more reliable model than ELM and GWO in this study.

     

Optimal power flow of HVDC system using teaching–learning-based optimization algorithm

In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching–learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method.

     

Optimal control of time-varying singular systems using the RK–Butcher algorithm

The problem of optimal control of time-varying linear singular systems with quadratic performance index has been studied using the Runge–Kutta–Butcher algorithm. The results obtained using the Runge–Kutta (RK) method based on the arithmetic mean (RKAM) and the RK–Butcher algorithms are compared with the exact solutions of the time-varying optimal control of linear singular systems. It is observed that the result obtained using the RK–Butcher algorithm is closer to the true solution of the problem. Stability regions for the RKAM algorithm, the single-term Walsh series method and the RK–Butcher algorithms are presented. Error graphs for the simulated results and exact solutions are presented in graphical form to highlight the efficiency of the RK–Butcher algorithm. This algorithm can easily be implemented using a digital computer. An additional advantage of this method is that the solution can be obtained for any length of time for this type of optimal control of time-varying linear singular systems.     

Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm

Modern machining processes are now-a-days widely used by manufacturing industries in order to produce high quality precise and very complex products. These modern machining processes involve large number of input parameters which may affect the cost and quality of the products. Selection of optimum machining parameters in such processes is very important to satisfy all the conflicting objectives of the process. In this research work, a newly developed advanced algorithm named ‘teaching–learning-based optimization (TLBO) algorithm’ is applied for the process parameter optimization of selected modern machining processes. This algorithm is inspired by the teaching–learning process and it works on the effect of influence of a teacher on the output of learners in a class. The important modern machining processes identified for the process parameters optimization in this work are ultrasonic machining (USM), abrasive jet machining (AJM), and wire electrical discharge machining (WEDM) process. The examples considered for these processes were attempted previously by various researchers using different optimization techniques such as genetic algorithm (GA), simulated annealing (SA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), harmony search (HS), shuffled frog leaping (SFL) etc. However, comparison between the results obtained by the proposed algorithm and those obtained by different optimization algorithms shows the better performance of the proposed algorithm.     

Stabilization of Rössler chaotic dynamical system using fuzzy logic control algorithm

This paper proposes a fuzzy logic control algorithm (FLCA) to stabilize the Rössler chaotic dynamical system. The fuzzy logic control system is based on a Takagi-Sugeno-Kang inference engine and the stability analysis in the sense of Lyapunov is carried out using Lyapunov’s direct method. The new FLCA is formulated to offer sufficient inequality stability conditions. The asymptotic complexity of our algorithm is analyzed and proved to be lower in comparison with that of linear matrix inequality-based FLCAs. A set of simulation results illustrates the effectiveness of the proposed FLCA.     

A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters

An important issue in application of fuzzy inference systems (FISs) to a class of system identification problems such as prediction of wave parameters is to extract the structure and type of fuzzy if–then rules from an available input–output data set. In this paper, a hybrid genetic algorithm–adaptive network-based FIS (GA–ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs). The parameters of a subtractive clustering method, by which the number and structure of fuzzy rules are controlled, are optimized by GAs within which ANFIS is called for tuning the parameters of rule base generated by GAs. The model has been applied in the prediction of wave parameters, i.e. wave significant height and peak spectral period, in a duration-limited condition in Lake Michigan. The data set of year 2001 has been used as training set and that of year 2004 as testing data. The results obtained by the proposed model are presented and analyzed. Results indicate that GA–ANFIS model is superior to ANFIS and Shore Protection Manual (SPM) methods in terms of their prediction accuracy.     

Numerical strategies for the system of first-order IVPS using the RK–Butcher algorithm

In this article, a new method of analysis for first-order initial-value type ordinary differential equations using the Runge–Kutta (RK)–Butcher algorithm is presented. To illustrate the effectiveness of the RK–Butcher algorithm, 10 problems have been considered and compared with the RK method based on arithmetic mean, and with exact solutions of the problems, and are found to be very accurate. Stability analysis for the first-order initial-value problem (IVP) has been discussed. Error graphs for the first-order IVPs are presented in a graphical form to show the efficiency of this RK–Butcher method. This RK–Butcher algorithm can be easily implemented in a digital computer and the solution can be obtained for any length of time.     

Pedestrian trajectory prediction using BiRNN encoder–decoder framework*

Autonomous mobile robots navigating through human crowds are required to foresee the future trajectories of surrounding pedestrians and accordingly plan safe paths to avoid any possible collision. This paper presents a novel approach for pedestrian trajectory prediction. In particular, we developed a new method based on an encoder–decoder framework using bidirectional recurrent neural networks (BiRNN). The difficulty of incorporating social interactions into the model has been addressed thanks to the special structure of BiRNN enhanced by the attention mechanism, a proximity-independent model of the relative importance of each pedestrian. The main difference between our and the previous approaches is that BiRNN allows us to employs information on the future state of the pedestrians. We tested the performance of our method on several public datasets. The proposed model outperforms the current state-of-the-art approaches on most of these datasets. Furthermore, we analyze the resulting predicted trajectories and the learned attention scores to prove the advantages of BiRRNs on recognizing social interactions.     

Development of an artificial muscle linear actuator using ionic polymer–metal composites

We are developing an artificial muscle linear actuator using ionic polymer-metal composites (IPMC)—electro-active polymers that bend in response to electric stimuli—and the goal of our study is applying the actuator to robotic applications, especially to a biped walking robot. In this paper, we will describe the structure of the actuator and an empirical model of the actuator which has two inputs and one output, and whose parameters are identified from input-output data. Based on the empirical model, basic experiments and position control of the linear actuator are demonstrated. Then, we consider walking control of a small-sized biped walking robot. In the application we assume that the developed actuators are connected both in series and in parallel to a joint of the walking robot so that the actuators supply enough torque to the robot, and that they are stretched and compressed enough. It is shown throughout simulations that the biped walking robot with the actuators can walk on level ground with a period synchronized with the period of the input signal.     

Modification of a two-dimensional fast Fourier transform algorithm with an analog of the Cooley–Tukey algorithm for image processing

Two-dimensional fast Fourier transform (FFT) for image processing and filtering is widely used in modern digital image processing systems. This paper concerns the possibility of using a modification of two-dimensional FFT with an analog of the Cooley–Tukey algorithm, which requires a smaller number of complex addition and multiplication operations than the standard method of calculation by rows and columns.     

Dynamic protein–protein interaction networks construction using firefly algorithm

Protein–protein interaction (PPI) networks are dynamic in the real world. That is, at different times and under different conditions, the interaction among proteins may or may not be active. In different dataset, PPI networks might be gathered as static or dynamic networks. For the conversion of static PPI networks to time graphs, i.e., dynamic PPI networks, additional information like gene expression and gene co-expression profiles is used. One of the challenges in system biology is to determine appropriate thresholds for converting static PPI networks to dynamic PPI networks based on active proteins. In the available methods, fixed thresholds are used for all genes. However, the purpose of this study is to determine an adaptive unique threshold for each gene. In this study, the available additional information at different times and conditions and gold-standard protein complexes was employed to determine fitting thresholds. By so doing, the problem is converted into an optimization problem. Thereafter, the problem is solved using the firefly meta-heuristic optimization algorithm. One of the most remarkable aspects of this study is determining the attractiveness function in the firefly algorithm. In this study, attraction is defined as a combination of standard complexes and gene co-expressions. Then, active proteins are specified utilizing the created thresholds. The MCL, ClusterOne, MCODE and Coach algorithms are used for final evaluation. The experimental results about BioGRID dataset and CYC2008 gold-standard protein complexes indicated that the produced dynamic PPI networks by the proposed method have better results than the earlier methods.     

Multi-objective optimization of machining and micro-machining processes using non-dominated sorting teaching–learning-based optimization algorithm

Selection of optimum machining parameters is vital to the machining processes in order to ensure the quality of the product, reduce the machining cost, increasing the productivity and conserve resources for sustainability. Hence, in this work a posteriori multi-objective optimization algorithm named as Non-dominated Sorting Teaching–Learning-Based Optimization (NSTLBO) is applied to solve the multi-objective optimization problems of three machining processes namely, turning, wire-electric-discharge machining and laser cutting process and two micro-machining processes namely, focused ion beam micro-milling and micro wire-electric-discharge machining. The NSTLBO algorithm is incorporated with non-dominated sorting approach and crowding distance computation mechanism to maintain a diverse set of solutions in order to provide a Pareto-optimal set of solutions in a single simulation run. The results of the NSTLBO algorithm are compared with the results obtained using GA, NSGA-II, PSO, iterative search method and MOTLBO and are found to be competitive. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for real production systems.     

Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm

Teaching–learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching–learning process. In the present work, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of a two stage thermoelectric cooler (TEC). Two different arrangements of the thermoelectric cooler are considered for the optimization. Maximization of cooling capacity and coefficient of performance of the thermoelectric cooler are considered as the objective functions. An example is presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization obtained by using the modified TLBO are validated by comparing with those obtained by using the basic TLBO, genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms.     

Heuristics free group method of data handling algorithm of generating optimal partial polynomials with application to air pollution prediction†

In this paper a revised GMDH (Group Method of Data Handling) algorithm is developed in which heuristicsare not required such as dividing the available date. into training data and checking data, predetermining the structure of the partial polynomials, or predetermining the number of intermediate variables. In this algorithm the prediction error criterion, such as PSS (Prediction Sum of Squares) or AIC (Akaike's Information Criterion) evaluated from all the available data, in used as a criterion for generating optimal partial polynomials, for selecting intermediate variables and for stopping the multilayered iterative computation. This heuristics freeGMDH algorithm is applied to non-linear modelling for short-term prediction of air pollution concentration. By using the time series data of SO₂, concentration, the wind velocity and the wind direction in Tokushima; Japan, a suitable model for predicting SO₂concentration at a few hours in advance is developed. The predicted results obtained by the revised GMDH model are compared with the results obtained by a linear regression model, a linear autoregressive model and a. basic GMDH model.     

Plus–minus algorithm — A method for derivation of the Bäcklund transformations

A new way of deriving Bäcklund transformations for nonlinear partial differential evolution equations is presented and applied to the following equations: Korteweg–de Vries, Gardner, Burgers, generalized KdV and the fifth order equations of the KdV hierarchies. The presented method is based on the assumption of the existence of particular forms of the Bäcklund transformations. This assumption is supported by the strong or semi-strong necessary condition concepts [Sokalski, K., Wietecha, T., Lisowski, Z., 2001. Acta Phys. Polon. B32, 17; Sokalski, K., Wietecha, T., Lisowski, Z., 2002. Int. J. Theor. Phys. Group Theory Nonlinear Opt., NOVA, 9, 331; Sokalski, K., Wietecha, T., Lisowski, Z., 2001. Acta Phys. Polon. B32, 2771; Sokalski, K., Wietecha, T., Sokalska, D. 2005. J. Nonlinear Math. Phys. 12, 31]. Its general form has been put within the framework of an algorithm and implemented in MAPLE.