Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach's results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.

Ontology-based semantic similarity: A new feature-based approach

This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.

https://romisatriawahono.net/lecture/rm/survey/softcomputing/Kar%20-%20Neuro%20Fuzzy%20Systems%20-%202014.pdf

Review Article: Applications of neuro fuzzy systems: A brief review and future outline

Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented. Temperatures and pressures of lines have been set as input variable of network and oil flow rate as output. In this case a 1600 data set of 50 wells in one of the northern Persian Gulf oil fields of Iran were used to build a database. ICA-ANN can be used as a reliable alternative way without personal and environmental problems. The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model. The results prove the effectiveness, robustness and compatibility of the ICA-ANN model.

Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

The inherent flexibility of hierarchical structure scheme with main-servo loop control structure is proposed to the problem of integrated chassis control system for the vehicle. It includes both main loop, which calculates and allocates the aim force using the optimal robust control algorithm and servo loop control systems, which track and achieve the target force using the onboard independent brake actuators. In fact, for the brake actuator, the aim friction is obtained by tracking the corresponding slip ratio of target force. For the coefficient of tire-road friction varying with different road surface, to get the nonlinear time-varying target slip ratio, the most famous quasi-static magic formula is proposed to estimate and predict real-time coefficient of different road surface and the constrained hybrid genetic algorithm (GA) is used to identify the key parameters of the magic formula on-line. Then, a self-tuning longitudinal slip ratio controller (LSC) based on the nonsingular and fast terminal sliding mode (NFTSM) control method is designed to improve the tracking accuracy and response speed of the actuators. At last, the proposed integrated chassis control strategies and the self-tuning control strategies are verified by computer simulations.

Study on Self-Tuning Tyre Friction Control for Developing Main-Servo Loop Integrated Chassis Control System

The paper surveys methods and approaches for the task of keyword extraction. The systematic review of methods was gathered which resulted in a comprehensive review of existing approaches. Work related to keyword extraction is elaborated for supervised and unsupervised methods, with a special emphasis on graph-based methods. Various graph-based methods are analyzed and compared. The paper provides guidelines for future research plans and encourages the development of new graph-based approaches for keyword extraction.

/pdf/an-overview-of-graph-based-keyword-extraction-methods-and-24d8d45jtt.pdf

An Overview of Graph-Based Keyword Extraction Methods and Approaches

In this paper, the stability analysis of the GA-based adaptive fuzzy sliding model controller for a nonlinear system is presented First, an uncertain and nonlinear plant for the tracking of a reference trajectory is well approximated and described via the reference model and the fuzzy model involving fuzzy logic control rules Next, the difficulty in designing a fuzzy sliding mode controller (FSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector The initial values of the consequent parameter vector are decided via the genetic algorithm After this, a modified adaptive law can be adopted to find the best high-performance parameters for the fuzzy sliding model controller The adaptive fuzzy sliding model controller is derived to simultaneously stabilize and control the system The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov's direct method Finally, a numerical simulation is provided as an example to demonstrate the control methodology

GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems

Generally, the biggest difficulty when designing a neural network controller that will be capable of rapidly and efficiently controlling complex and nonlinear systems is selection of the most appro...

Stabilization of adaptive neural network controllers for nonlinear structural systems using a singular perturbation approach

In this paper, the stability analysis of a genetic algorithm-based (GA-based) H∞ adaptive fuzzy sliding model controller (AFSMC) for a nonlinear system is discussed First, a nonlinear plant is well-approximated and described with a reference model and a fuzzy model, both involving fuzzy logic control (FLC) rules Then, FLC rules and the consequent parameter are decided on via a genetic algorithm After this, we guarantee a new H∞ tracking performance inequality for the control system The H∞ tracking problem is characterized to solve an eigenvalue problem Next, an AFSMC is proposed to stabilize the system so as to achieve good H∞ control performance Lyapunov’s direct method can be used to ensure the stability of the nonlinear system It is shown that the stability analysis can reduce nonlinear systems into a linear matrix inequality problem Finally, a numerical simulation is provided to demonstrate the control methodology

Linear Matrix Inequality Conditions of Nonlinear Systems by Genetic Algorithm-based H ∞ Adaptive Fuzzy Sliding Mode Controller

Generally, the difficulty with multivariable system control is how to overcome the coupling effects for each degree of freedom. The computational burden and dynamic uncertainty of multivariable systems makes the model-based decoupling approach hard to implement in a real-time control system. In this study, an intelligent adaptive controller is proposed to handle these behaviors. The structure of these model-free new controllers is based on fuzzy systems for which the initial parameter vector values are found based on the genetic algorithm. One modified adaptive law is derived based on Lyapunov stability theory to control the system for tracking a user-defined reference model. The requirement of the Kalman–Yacubovich lemma is fulfilled. In addition, a non-square multivariable system can be decoupled into several isolated reduced-order square multivariable subsystems by using the singular perturbation scheme for different time-scale stability analysis. The adjustable parameters for the intelligent system can be initialized using a genetic algorithm. Novel online parameter tuning algorithms are developed based on the Lyapunov stability theory. A boundary-layer function is introduced into these updating laws to cover parameter and modeling errors and to guarantee that the state errors converge into a specified error bound. Finally, a numerical simulation is carried out to demonstrate the control methodology that can rapidly and efficiently control nonlinear multivariable systems.

GA-based decoupled adaptive FSMC for nonlinear systems by a singular perturbation scheme

In this paper, we present a new approach to help users using search engines without entering any keywords. What we want to do is to predict what word the users may want to search before they think about it. Most of the studies done in this field focus on how to help users enter keywords or how to re-rank the search results in order to make them more precise. Both of those methods need to establish a user behavior model and a repository in which to save the logs. In our proposed method, we use the Google similarity distance to measure keywords in the Webpage to find the potential keywords for the users. Thus, we do not need any repository. All the executions are on-line and real-time. Then, we extract all the important keywords as the potential search keywords. In this way, we can use these professional keywords to achieve precise search results. We believe that this can be useful in many areas such as e-learning and can also be used in mobile devices.

P. C. Chen

Papers

GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems

Stabilization of adaptive neural network controllers for nonlinear structural systems using a singular perturbation approach

Linear Matrix Inequality Conditions of Nonlinear Systems by Genetic Algorithm-based H ∞ Adaptive Fuzzy Sliding Mode Controller

GA-based decoupled adaptive FSMC for nonlinear systems by a singular perturbation scheme

Automatic keyword prediction using Google similarity distance