Fuzzy Recursive Least-Squares Approach in Speech System Identification: A Transformed Domain LPC Model

This paper is motivated by the recognition that sources of uncertainties in the electric power systems are multifold and that they may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, uncertain price of electricity and hard-to-predict power produced by the renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. It is, therefore, becoming very important to develop modeling methods for predicting uncertain load and wind power, in particular. In this paper we first transform historic time-stamped data into their Fourier Transform (FT) representation. The frequency domain data representation is used to decompose the wind and load power signals and to derive predictive models relevant for short-term and long-term predictions. The short-term results are interpreted next as a Linear Prediction Coding Model (LPC) and its accuracy is analyzed. Next, the Discrete Markov Process (DMP) representation is applied to help assess probabilities of most likely short-, medium- and long-term states and the related multi-temporal risks. Throughout the paper we use publicly available data for the New York Control Area (NYCA).

The central point in designing a fuzzy system for modeling and control is the generation of a set of efficient and effective fuzzy rules. Generally, fuzzy rules can be generated either from heuristics or from experimental data. Heuristic fuzzy rule generation, as discussed in Chapter 1, consists of the following main steps: Determination of the structure of the fuzzy systems. This includes mainly the selection of the inputs and outputs. Usually, a multi-input multi-output fuzzy systems can be decomposed into a number of multi-input single-output fuzzy systems. Definition of linguistic terms and their fuzzy membership function for each fuzzy variables. If a Mamdani fuzzy system is designed, each input and output can be seen as a linguistic variable. Therefore, it is necessary to define the linguistic terms (e.g., Small, Medium, Large etc.) and their fuzzy membership function. Details about the definition of a linguistic variable is presented in Section 1.2.1. Determination of fuzzy inference method. For example, for fuzzy mapping rules, the fuzzy Cartesian product can be used to define the fuzzy relation. Refer to Section 1.2.2. Defuzzification. If the Mamdani type fuzzy rules are used, the fuzzy output derived from the fuzzy rule base should be defuzzified. Refer also to Section 1.2.2 for different defuzzification methods. If the Takagi-Sugeno fuzzy rules are used, the defuzzification is included in the fuzzy inference and a crisp output is obtained directly.

An algorithm for LPC (linear predictive coding) parameter optimization in multipulse (MP)-LPC based speech coders is presented. It is shown that, by taking into account the nature of the MP-excitation signal into LPC parameter computation, it is possible to improve the effectiveness of the LPC model. This results in a better quality of the reconstructed signal in terms both of objective and subjective criteria. The implementation details of the algorithm are discussed and experimental results are presented. In particular a comparison with standard MP-LPC techniques is given. >

Parameter estimation of a three-axis spacecraft simulator using recursive least-squares approach with tracking differentiator and Extended Kalman Filter

This article provides a novel framework for the concurrent development of asynchronous observers and controllers for discrete-time nonlinear semi-Markov jump singularly perturbed systems subjected to mismatched modes, states, and premise variables between controlled systems and observer-based controllers. Aiming at characterizing nonlinearity with parameter uncertainty, the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy technique is implemented in system modeling. Meanwhile, it is supposed that both observer and controller modes can just be acquired via a hidden Markov mode detector in the first attempt. Then, following the concept of non-parallel distribution compensation (non-PDC), the observer-based IT2 fuzzy asynchronous controllers are constructed with observers and controllers sharing the same fuzzy membership function but different from that in systems, which improves the designed flexibility. In accordance with semi-Markov kernel approach and the Lyapunov function contingent upon both system modes and sojourn times, sufficient criteria are established for the functioning of expected mode-dependent IT2 fuzzy observers and controllers such that the σ-mean-square stability for the resulting nonlinear augmented semi-Markov jump singularly perturbed systems comprised of the controlled systems and observation error systems is guaranteed. Furthermore, from the perspective of fuzzy processing, parameters and relaxation matrices that comply with fuzzy rules are added to ensure system stability while further reducing the conservatism of conditions. Ultimately, a circuit model and comparison examples are shown to substantiate the necessity and superiority of the suggested technique.

We examine the performance of compact fuzzy rule-based classification systems that consist of a small number of simple fuzzy rules with high comprehensibility. Those fuzzy systems are designed in a heuristic manner using rule selection criteria. We first describe fuzzy rule-based classification. Next we describe heuristic rule selection criteria using the terminology in data mining: confidence and support. A small number of fuzzy rules are extracted from numerical data based on each rule selection criterion. Then we examine the classification performance of extracted fuzzy rules through computational experiments on a number of benchmark data sets from the UCI ML Repository. Our results should be viewed as the lowest benchmark performance of fuzzy rule-based classification systems because fuzzy rules are extracted using a simple heuristic method with no optimization or tuning procedures. Nevertheless our results on some data sets are comparable to reported results by the C4.5 algorithm in the literature.

Fuzzy systems generally work based on expert knowledge base. Fuzzy expert knowledge base incorporates human knowledge through fuzzy rules and fuzzy membership functions. In designing fuzzy models, a major difficulty in the identification of an optimized fuzzy rules as well as membership function shape and type of an individual rule. In fact, it is difficult and time consuming for an expert to define a complete rule set for a complex system having a large number of parameters. In this paper, we proposed a flexible encoding method for evolutionary algorithm to discover parameters of fuzzy rule, design of a suitable fuzzy models and controller for a particular environment. In Evolutionary Fuzzy System (EFS), evolutionary algorithms are adapted for finding the optimal fuzzy rule sets including the number of rules inside it and selecting the membership function shape and type of each individual rule in two different ways respectively. The benefits of this methodology are illustrated for the modeling and control of nonlinear system that shows better performance than existing fuzzy expert systems.

Design of a fuzzy controller requires specification of both membership functions and decision rules. Specification of membership function for a fuzzy logic controller has been an important issue. The traditional way of selecting membership functions has been, in most cases, an adhoc procedure. In this paper, an optimization algorithm based on simulated annealing for designing fuzzy membership functions for fuzzy controllers is introduced. An optimization algorithm for designing the membership functions and fuzzy rule base for a fuzzy controller is presented. An optimal fuzzy controller using proposed technique for backing up a truck problem is designed and implemented, in which optimal fuzzy membership functions and fuzzy rules are designed. Also, a neural network controller for the same truck problem is designed to compare our results.

This paper introduced the self-tuning fuzzy PID controller based on particle swarm optimization which aims to gain more precise control over the position of pneumatic proportional valve barrel, where particle swarm works to optimize the membership function, fuzzy rule and PID parameter in fuzzy control. The study fruits also include online optimization of the self-tuning fuzzy PID controller parameters. Comparing to the conventional control methodology, The self-tuning fuzzy PID controller with PSO optimization is proven to show better precision, dynamic performance index and more rapid tracking performance and robustness. To this end, step response was used to compare and analyze the results from the PSO algorithm optimization, providing a pragmatic method of better comprehensive performance for PID parameter optimization.

A method is proposed to design a wide-area damping controller (WADC) based on fuzzy control to dampen the low-frequency oscillations of interconnected power systems. First, the inputs and expected outputs of a fuzzy logic controller are analyzed. Then, a universe of fuzzy sets, membership functions and fuzzy rules are determined based on the relationship between inputs and outputs, and the fuzzy logic controller is constituted. The WADC consists of a fuzzy logic controller and a gain. The gain is obtained using particle swarm optimization. A four-machine two-area power system is simulated using the Matlab/Simulink software to test the performance of the fuzzy-based WADC. The simulation results indicate that the designed controller can compensate for communication delay and improve interconnected power system damping.

Induction of fuzzy rules and membership functions from training examples

On the basis of PID engineering tuning methods, a fuzzy PID control algorithm based on the fuzzy rules is put forward. To design this kind of algorithm, firstly, simplify PID formula according to the Z-N and critical proportion methods, and then, construct a piece of fuzzy control table according to fuzzy control rules and fuzzy control reasoning. In the paper, three-dimensional fuzzy control is used, which is more predominant than that with two-dimension. In real-time control, by looking up fuzzy control table, the PID parameter can be adjusted on line. The simulation results of controlling the angle of the helm and course of the ship show that the algorithm raised in this paper has good performances such as simple operation, excellent personality, etc.

The study of Vehicle Health monitoring plays very important role in deciding the possibility of completing journey successfully. Already work has been done on Pre Trip plan assistance with vehicle Health Monitoring with simple and Hybrid Fuzzy controllers. The conventional Fuzzy logic controllers are knowledge-based systems, incorporating human knowledge into their knowledge base through fuzzy rules and fuzzy membership functions. They are limited in application, as its logic rules and membership functions have to be preset with expert knowledge and have a great influence over the performance of the fuzzy logic controller. In the present work, a Genetic Fuzzy system is proposed to promote the learning performance of logic rules. The resultant hybrid system seems to be highly adaptive and trained through a proper performance, hence is much more sophisticated and has a higher degree of adaptive parameters. The work proposes a benefit of Methodology by comparison of calculating safest distance by evaluating Vehicle Health Monitoring using Hybrid Fuzzy, and genetic fuzzy controller.

The traditional PID control method has the disadvantages of slow response speed, low precision and poor anti-interference ability when controlling the brushless DC motor system. In view of these shortcomings, a fuzzy PID control based brushless DC motor control system was proposed. The principle and characteristics of fuzzy control were introduced; The control principle of PID control based on fuzzy rules was analyzed; The fuzzy subsets were established and fuzzy control rules were established; The simulation of brushless DC motor control system was simulated by Matlab/Simulink. The results showed that the PID control algorithm based on fuzzy logic control improved the dynamic performance and stronger adaptive ability of the DC brushless motor.

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm