Abstract: The sliding mode AGC controller and method includes Genetic Algorithms (GA) to find the optimal feedback gains and switching vector values of the controller. In order to provide enhancement of the system dynamical performance and a reduction in the SMC chattering, two objective functions are provided in the optimization process. The tested two-area interconnected AGC model incorporates nonlinearities in terms of Generation Rate Constraint (GRC) and a limiter on the integral control value. Comparison with previous AGC methods reported in the literature validates the significance of the sliding mode AGC controller.

Abstract: The method of identifying Hammerstein models with known nonlinearity structures using particle swarm optimization provides a computerized method utilizing a particle swarm optimization (PSO)-based scheme for the identification of nonlinear Hammerstein models with known nonlinearity structures. Particularly, this is accomplished by formulating the identification of the Hammerstein model as an optimization problem, with PSO being used in the optimization process.

Abstract: The educational system for testing memorization provides a computerized classroom system for testing a student's memorization of a text to be recited, for example, the text of the Qur'an. A set of digital data representing a text of a written work to be memorized by a student, such as the text of the Qur'an, is recorded in a database. Upon selection of a text portion to be tested, the portion is divided into individual words, and the user recites the portion, with the audio input being received and recorded by the system. The audio input from the user is converted into textual data, which is compared with a corresponding word of the portion stored in the database. If the spoken word matches the corresponding word portion stored in the database, the word is instantly displayed on a computer display, and the user may then speak the next word.

Abstract: Contamination monitoring of high voltage insulators provides a system and method producing an early predictor for high voltage insulator failure, allowing repairmen to either already be on site when a high voltage insulator fails in order to expedite repair time, or allowing repair and/or replacement of a faulty insulator before the failure actually occurs. The system and method provide transmission of an alarm signal when contaminant levels (such as equivalent salt deposit density (ESDD) levels) formed on a high voltage insulator exceed pre-selected threshold values, indicating the likelihood of high voltage insulator failure.

Abstract: The identification of Hammerstein models relates to a computerized method for identifying Hammerstein models in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN), and in which a particle swarm optimization (PSO) algorithm is used to estimate the neural network parameters and a numerical algorithm for subspace state-space system identification (N4SID) is used for estimation of parameters of the linear part.

Abstract: The method for Hammerstein modeling of a steam generator plant includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN). Particle swarm optimization (PSO), typically known for its heuristic search capabilities, is used for estimating the parameters of the RBFNN. Parameters of the linear part are estimated using a numerical algorithm for subspace state-space system identification (N4SID).

Abstract: The method for the identifying of multiple input, multiple output (MIMO) Hammerstein models that includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN).

Abstract: The method of identifying Hammerstein models with known nonlinearity structures using particle swarm optimization provides a computerized method utilizing a particle swarm optimization (PSO)-based scheme for the identification of nonlinear Hammerstein models with known nonlinearity structures. Particularly, this is accomplished by formulating the identification of the Hammerstein model as an optimization problem, with PSO being used in the optimization process.

Abstract: The computerized method for identifying Hammerstein models is a method in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN). Accurate identification of a Hammerstein model requires that output error between the actual and estimated systems be minimized. Thus, the problem of identification is an optimization problem. A hybrid algorithm, based on least mean square (LMS) principles and the Subspace Identification Method (SIM) is developed for the identification of the Hammerstein model. LMS is a gradient-based optimization algorithm that searches for optimal solutions in the negative direction of the gradient of a cost index. In the method, LMS is used for estimating the parameters of the RBFNN. For estimation of state-space matrices, the N4SID algorithm for subspace identification is used.

Abstract: The neural network optimizing sliding mode controller includes an adaptive SMC that overcomes the limitations imposed on the effectiveness of the SMC under different operating conditions. Neural networks are used for on-line prediction of the optimal SMC gains when the operating point changes. The controller can be applied to a power system stabilizer (PSS) of a single machine power system. Simulation results demonstrate the effective performance of the neural network optimizing sliding mode controller.

Abstract: The sliding mode AGC controller and method includes Genetic Algorithms (GA) to find the optimal feedback gains and switching vector values of the controller. In order to provide enhancement of the system dynamical performance and a reduction in the SMC chattering, two objective functions are provided in the optimization process. The tested two-area interconnected AGC model incorporates nonlinearities in terms of Generation Rate Constraint (GRC) and a limiter on the integral control value. Comparison with previous AGC methods reported in the literature validates the significance of the sliding mode AGC controller.

Abstract: The particle swarm optimizing sliding mode controller is applied to an interconnected Automatic Generation Control (AGC) model. The system formulates the SMC design as an optimization problem and utilizes a Particle Swarm Optimization (PSO) algorithm to find the optimal feedback gains and switching vector values of the controller. Two performance functions are used in the optimization process to demonstrate the system dynamical performance and SMC chattering reduction. The tested two-area interconnected AGC model incorporates nonlinearities in terms of Generation Rate Constraint (GRC) and a limiter on the integral control value.

Abstract: Contamination monitoring of high voltage insulators provides a system and method producing an early predictor for high voltage insulator failure, allowing repairmen to either already be on site when a high voltage insulator fails in order to expedite repair time, or allowing repair and/or replacement of a faulty insulator before the failure actually occurs. The system and method provide transmission of an alarm signal when contaminant levels (such as equivalent salt deposit density (ESDD) levels) formed on a high voltage insulator exceed pre-selected threshold values, indicating the likelihood of high voltage insulator failure.

Abstract: The computerized method for identifying Hammerstein models is a method in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN). Accurate identification of a Hammerstein model requires that output error between the actual and estimated systems be minimized. Thus, the problem of identification is an optimization problem. A hybrid algorithm, based on least mean square (LMS) principles and the Subspace Identification Method (SIM) is developed for the identification of the Hammerstein model. LMS is a gradient-based optimization algorithm that searches for optimal solutions in the negative direction of the gradient of a cost index. In the method, LMS is used for estimating the parameters of the RBFNN. For estimation of state-space matrices, the N4SID algorithm for subspace identification is used.

Abstract: The identification of Hammerstein models relates to a computerized method for identifying Hammerstein models in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN), and in which a particle swarm optimization (PSO) algorithm is used to estimate the neural network parameters and a numerical algorithm for subspace state-space system identification (N4SID) is used for estimation of parameters of the linear part.

Abstract: The method for Hammerstein modeling of a steam generator plant includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN). Particle swarm optimization (PSO), typically known for its heuristic search capabilities, is used for estimating the parameters of the RBFNN. Parameters of the linear part are estimated using a numerical algorithm for subspace state-space system identification (N4SID).