Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification

The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. The flexibility of a genetic algorithm allows various strategies to be applied to it. One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. The strategy has been shown to be better than the simple genetic algorithm and conventional statistical method, but it contains inadequate justification of how the separation is made. The usage of objective function values for separation of groups does not carry much flexibility and is not suitable since different time-dependent data have different levels of equilibrium and thus different ranges of objective function values. This paper investigates the optimum grouping of chromosomes by fixed group ratios, enabling more efficient identification of dynamic systems using a NARX (Non-linear AutoRegressive with eXogenous input) model. Several simulated systems and real-world timedependent data are used in the investigation. Comparisons based on widely used optimization performance indicators along with outcomes from other research are used. The issue of model parsimony is also addressed, and the model is validated using correlation tests. The study reveals that, when recombination and mutation are used for different groups, equal composition of both groups produces a better result in terms of accuracy, parsimony, speed, and consistency

Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification

System identification is a method of determining a mathematical relation between variables and terms of a process based on observed input-output data. Model structure selection is one of the important steps in a system identification process. Evolutionary computation (EC) is known to be an effective search and optimization method and in this paper EC is proposed as a model structure selection algorithm. Since EC, like genetic algorithm, relies on randomness and probabilities, it is cumbersome when constraints are present in the search. In this regard, EC requires the incorporation of additional evaluation functions, hence, additional computation time. A deterministic mutation-based algorithm is introduced to overcome this problem. Identification studies using NARX (Nonlinear AutoRegressive with eXogenous input) models employing simulated systems and real plant data are used to demonstrate that the algorithm is able to detect significant variables and terms faster and to select a simpler model structure than other well-known EC methods

EFFECT OF PENALTY FUNCTION PARAMETER IN OBJECTIVE FUNCTION OF SYSTEM IDENTIFICATION

The evaluation of an objective function for a particular model allows one to determine the optimality of a model structure with the aim of selecting an adequate model in system identification. Recently, an objective function was introduced that, besides evaluating predictive accuracy, includes a logarithmic penalty function to achieve a suitable balance between the former model’s characteristics and model parsimony. However, the parameter value in the penalty function was made arbitrarily. This paper presents a study on the effect of the penalty function parameter in model structure selection in system identification on a number of simulated models. The search was done using genetic algorithms. A representation of the sensitivity of the penalty function parameter value in model structure selection is given, along with a proposed mathematical function that defines it. A recommendation is made regarding how a suitable penalty function parameter value can be determined

Evolutionary Computation in System Identification: Review and Recommendations

Two of the steps in system identification are model structure selection and parameter estimation. In model structure selection, several model structures are evaluated and selected. Because the evaluation of all possible model structures during selection and estimation of the parameters requires a lot of time, a rigorous method in which these tasks can be simplified is usually preferred. This paper reviews cumulatively some of the methods that have been tried since the past 40 years. Among the methods, evolutionary computation is known to be the most recent one and hereby being reviewed in more detail, including what advantages the method contains and how it is specifically implemented. At the end of the paper, some recommendations are provided on how evolutionary computation can be utilized in a more effective way. In short, these are by modifying the search strategy and simplifying the procedure based on problem a priori knowledge

Deterministic Mutation Algorithm As A Winner Over Forward Selection Procedure

System identification is a field of study involving the derivation of a mathematical model to explain the dynamical behaviour of a system. One of the steps in system identification is model structure selection which involves the selection of variables and terms of a model. Several important criteria for a desirable model structure include its accuracy in future prediction and model parsimony. A parsimonious model structure is desirable in enabling easy control design. Two methods of model structure selection are closely looked into and these are deterministic mutation algorithm (DMA) and forward selection procedure (FSP). The DMA is known to be originated from evolutionary computation whereas FSP may be listed under the study of regression. They have close similarities in characteristics, more specifically known as forward search in model structure selection. However, both also function in a population-based optimization and statistical approaches, respectively. Due to the closeness, this research attempts to clarify the advantages and disadvantages of both methods through model structure selection of difference equation model in system identification. Simulated and real data were used. To allow for fair comparison, DMA was altered so as to equalize its strength, where applicable, to that of FSP. In the real data simulation, both methods obtained the same model structure whereas in simulated data modelling, only DMA was able to select the correct model structure. This concludes that DMA not only has the advantage of simpler procedure but it also superseded the performance of FSP, even with a handicapped alteration

Reduction Of Corrosion Rate Of Aluminium Alloy 6061 Through Anodization

This paper focuses on reducing the corrosion rate of aluminium alloy 6061 through anodizing. The study involves characterizing corrosion phenomenon that occurs on aluminium alloy 6061 in relation to parameters involved in an anodizing process, in particular the current density of anodizing, and its corrosion environment; specifically, the concentration and pH value of the corrosion accelerator. The experiment samples were anodized in sulphuric acid (H2SO4) at a current density ranging from 0.012 A/mm2 to 0.018 A/mm2. The paper also includes a qualitative analysis of corrosion images ob-tained from the experiment through scanning electron microscope. It concludes that corrosion rate may be reduced through an increase of current density during anodizing

Application of design for manufacturing and assembly (DFMA) method to vehicle door design

Design for manufacturing and assembly (DFMA) guidelines aim to reduce part count, number of welds and number of operations. By doing so, production advantages such as shorter production time, higher management efficiency and greater customer satisfaction are achieved. In this paper, the effectiveness of the DFMA method was shown in vehicle door design. Two vehicle door designs were taken apart to investigate the feasibility of better designs using the Boothroyd and Dewhurst analysis. It employed quantitative analysis of various parts of the design, such as door frame, door board and screws. Each part of the design was rated with a numerical value depending on its assembly requirements. The product was then redesigned, using the numerical values as a goal to be minimised. Various factors concerning assembly were considered, such as symmetry and size of part. The outcome was designs that have shorter assembly time and assembly efficiency higher than 15%

Hardware-In-The-Loop Simulation Of Steer-By-Wire System In Automotive Vehicle

Field test approach of steer-by-wire (SBW) technologies by using actual vehicle can be very dangerous. This is due to the fact that stability of the vehicle is very sensitive to the steering wheel input. Less optimum parameters of the steering controllers or system failure may lead to dangerous road accident. In this study, hardware-in-the-loop simulation (HiLS) is used to bridge the gap between simulation and experimentation of SBW system. In the proposed HiLS system, SBW test rig is set up to communicate in real time with 14 degree-of-freedom vehicle model. Proportional-integral-derivative control optimized with Ziegler-Nichols method is used to control the stepper motor of the SBW test rig. From simulation and experimental results, SBW system developed has the ability to closely follow the steering trajectory of the conventional steering system with acceptable errors. By using HiLS, both controller algorithm and the functionality of the steering actuator of SBW system can be tested in a semi-real driving condition as preliminary testing

Discrete-Time System Identification Based On Novel Information Criterion Using Genetic Algorithm

Model structure selection is a problem in system identification which addresses selecting an adequate model i.e. a model that has a good balance between parsimony and accuracy in approximating a dynamic system. Parameter magnitude-based information criterion 2 (PMIC2), as a novel information criterion, is used alongside Akaike information criterion (AIC). Genetic algorithm (GA) as a popular search method, is used for selecting a model structure. The advantage of using GA is in reduction of computational burden. This paper investigates the identification of dynamic system in the form of NARX (Non-linear AutoRegressive with eXogenous input) model based on PMIC2 and AIC using GA. This shall be tested using computational software on a number of simulated systems. As a conclusion, PMIC2 is able to select optimum model structure better than AIC

Parameter Magnitude-Based Information Criterion in Identification of Discrete-Time Dynamic System / Md Fahmi Abd Samad and Abdul Rahman Mohd Nasir

Information criterion is an important factor for model structure selection in system identification. It is used to determine the optimality of a particular model structure with the aim of selecting an adequate model. A good information criterion not only evaluate predictive accuracy but also the parsimony of model. There are many information criterions those are widely used such as Akaike information criterion (AIC), corrected Akaike information criterion (AICc) and Bayesian information criterion (BIC). This paper introduces a new parameter-magnitude based information criterion (PMIC2) for identification of linear and non-linear discrete time model. It presents a study on comparison between AIC, AICc, BIC and PMIC2 in selecting the correct model structure for simulated models. This shall be tested using computational software on a number of simulated systems in the form of discrete-time models of various lag orders and number of terms/variables. It is shown that PMIC2 performed in optimum model structure selection better than AIC, AICc and BIC