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Recently, nonlinear system identification has received increasingly more attention due to its promising applications in engineering fields. It has become a challenging task to truly apply this system due to many complex factors especially the nonlinear and dynamical properties. The objective of this paper is to design a nonlinear system identification method using an appropriate learning method. This paper proposes a biogeography-based optimization (BBO)-based multilayer perceptron (MLP) architecture for nonlinear system identification. The BBO algorithm with its habitats imitates the species migration between them. A good solution is featured by an island with a higher High Suitability Index (HSI), and a poor solution by an island with a lower HSI. Higher HSI solutions resist change more effectively than lower HSI solutions. By combining the two schemes, the proposed MLP architecture with BBO learning provides a promising scheme for nonlinear system identification. Three kinds of nonlinear system are adopted for experimental verification, including Mackey–Glass series, Henon system, and nonlinear plant system. Mean squared error (MSE) index is used to calculate the difference between the measured input and output of the systems. By employing the nonlinear cases, the proposed algorithm presents rapid convergence and excellent MSE in nonlinear system identification. [ABSTRACT FROM AUTHOR]

A modified second-order sliding mode control (MSOSMC) combined with radial basis function (RBF) network estimator is developed and proposed to achieve accurate speed tracking performance for synchronous reluctance motor (SynRM). The dynamic model of SynRM system has the properties of parameter variations, external disturbance, and nonlinear friction force. The MSOSMC method that utilizes continuous control input is applied to reduce the chattering phenomenon. Also, this method utilizes two sliding surfaces to solve the problem of system uncertainty and reduce motor power consumption. The RBF network is developed in MSOSMC scheme to estimate the lumped uncertainty in an on-line fashion. The proposed MSOSMC method uses the system error and control input as the convergence criteria. The adaptation scheme adjusts the parameter vectors based on the Lyapunov theorem approach, so that the asymptotic stability of the developed motor system can be guaranteed. Experimental results show that the MSOSMC structure achieves the better tracking performances in terms of root-mean-square error compared with the traditional SOSMC method under different speed tracking conditions. [ABSTRACT FROM AUTHOR]

Real systems with nonlinearity and valuable information always have unpredictable meaning and chaos properties in nature. It is often difficult to obtain the system model based on set of stimulus and response samples due to its complexity. This paper proposes the type-2 fuzzy neural network (T2FNN) and its learning method using grey wolf optimizer (GWO) for system identification. The structure of T2FNN is a combination between a type-2 fuzzy logic system with human-like IF-THEN rule thinking properties and neural networks (NN) with learning and optimization capability. The GWO is a meta-heuristic algorithm inspired from the social hunting behavior of grey wolves as search agents. This method is investigated by strategy, which maintains a proper balance between exploration and exploitation processes and gives more importance to the fittest to find the new position of search agent during the iterations. The GWO algorithm is considered for learning method due to its advantages, including high accuracy, very effective and more competitive. To evaluate the proposed system, the T2FNN with GWO learning method is employed by several nonlinear problems, including single-input single-output systems and multi-input multi-output (MIMO) systems. They are Mackey-Glass chaotic data series, nonlinear plant, nonlinear dynamical time-varying plant, and two MIMO systems. The merits of the proposed learning algorithm can be defined through experiments of the model and compared with existing learning PSO and GA algorithms and previous studies in literature. Based on the experimental results and performance comparisons, the proposed GWO algorithm indeed obtains the enhanced performance and capability in terms of the root mean squared error for nonlinear system identification applications. [ABSTRACT FROM AUTHOR]

The inertia of the stacked multi-degree-of-freedom platform imposes limitation on the achievable motion speed. To overcome this barrier, the unstable design becomes very attractive. This research presents a novel single-deck dual-axis linear motor platform that is capable of fast long-range motion. The platform is equipped with air-bearing design to achieve backlash-free operation. As a result, the positioning rigidity depends entirely on the performance of the servo system. A force distribution control is used to decouple the difference axis dynamics. This research also proposes a chain scattering description (CSD) setup to allow a pseudo-derivative feedback (PDF) control framework that is commonly used in industrial motion control for achieving high servo rigidity. An interesting finding is that our experiments show that without the help of complex piggyback piezoelectric stage, the single-deck platform design, with a stroke as large as 20 mm, can already reach a servo accuracy of ±60 and ±80 nm in the x- and y-axis and 600 μarcsec in the yaw axis. [ABSTRACT FROM AUTHOR]

The paper presents the implementation of a Nios-based colonoscopy navigation system. The navigation system is designed to help medical doctors to perform a better colonoscopy. During the process of inserting the endoscope into a patient's intestine, this system can guide the doctor to adjust the endoscope's angle. The system generates instructions based on the cavity of the intestine and the orientations of colon folds. A field programmable gate arrays platform is used to implement this system. Verilog is adopted to design the circuits to calculate the cavity direction. The system on programmable chip built in the system runs the C code for calculating the colon folds' orientations. After all calculations are completed, the navigation system displays the guiding instruction on a screen with the original video. The implementation of the system is discussed in this paper. The output image shows that the system functions very well. [ABSTRACT FROM AUTHOR]

An ultra-high-speed motor driver with hybrid modulations is proposed in this paper. The speed range of a spindle motor is wider and trends to ultra-high, but the traditional driver which provides either sinusoidal pulse width modulation or six-step modulation could not support so wide speed range. Normally, the variable voltage variable frequency control is used to support different type motors, then, sinusoidal pulse width modulation (SPWM) is chosen in low speed range drivers and six-step is picked in high-speed application. In this paper, the hybrid modulation driver with adjustable DC bus voltage is proposed, and the modulation could be selected for different motor and speed by users. For lower switching power loss, the six-step modulation is better when ultra-high-speed application. For low speed range, the trapezoidal pulse width modulation is used to replace the SPWM for the reduction of switching power loss. The experimental results show the performance of proposed driver. [ABSTRACT FROM AUTHOR]

In this study, we analyze the expiration day effects of index futures on the cash market in Taiwan, and find that both volatility and trading volume are higher on the final settlement days than on other trading days. We also calculate the volume of open interest for the final settlement of index futures contracts relating to different classes of traders, as well as the profits they earn from their open interest positions. We find that proprietary traders exhibit superior performance whereas foreign investors achieve the worst returns. Our empirical results support the view that the expiration day effects in the Taiwan futures market are at least partially attributable to attempts at 'marking the close'. [ABSTRACT FROM AUTHOR]

This paper proposes a novel circuit for correcting the errors occurring in the speed measurement of a brushless DC (BLDC) motor with Hall sensors that are built in for commutation. Although the poles of the rotor are always placed with a uniform angular separation, the Hall sensors may not. The angular misalignment between the poles and the Hall sensors will cause errors in the speed measurement. With a method previously proposed to estimate the degree of misalignment, the speed errors can be minimized by our proposed circuit. This circuit is implemented in an FPGA with the aid of a hardware description language and is validated by the simulated results. [ABSTRACT FROM AUTHOR]