An attention infused multi-stage parallel adaptive neuro fuzzy systems framework with metaheuristic optimization for accurate water quality prediction

Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %.

Forecasting the level of reservoir has been a significant subject in the management of reservoirs and water resource. For many years, estimation of reservoir water level was primary based on operator’s experience, curves and mathematical models. Recently, Artificial Intelligence (AI) methods are developed in several hydrological aspects, such as classification and forecasting parameters. The major advantage of AI modeling is the considerable ability to map input-output pattern without requiring prior knowledge about the factors that affect the forecasting parameters. This study attempts to forecast the daily level of Klang Gate dam using adaptive neuro fuzzy interface system (ANFIS) in two different scenarios and various time delays in inputs. In the first scenario, daily rainfall is used solely as an input in different time delays from the time (t) to the time (t-4) that is illustrated in spite of the reasonable performance of error, less than 10% of solely rainfall data could not have reasonable response in fluctuations to forecast accurately. Increasing the level of reservoir beside precipitation as inputs in both sets of models could enhance the fitness of the estimated and observed data dramatically. Due to the fact that the distance of gauges of stations are unknown, using various models in different time delays of inputs could demonstrate the distance between gauges; moreover, it shows the reasonable duration in inputs and outputs to have accurate prediction. Key words: Klang Gate, adaptive neuro fuzzy interface system (ANFIS), forecasting model.

This paper describes an architecture-based dynamic adaptation model and framework for adaptive software systems. The framework provides for a reusable adaptation infrastructure and uses a layered architecture pattern. It also provides separation of concerns from the system's software architecture and supports internal state information checkpointing and restoration. Any dynamic software adaptation process, whether instigated internally or externally, makes runtime changes only to affected components. The dynamic adaptation model separates adaptation-impacted parts of a system from those that need not be concerned with the dynamic adaptation.

This paper represents a new approach to manage learner modeling in an adaptive learning system framework. It considers developing the basic components of an adaptive learning system such as the learner model, the course content model and the adaptation engine. We use the overlay model and Bayesian network to evaluate learnersâ?? knowledge. In addition, we also propose a new content modeling method as well as adaptation engine to generate adaptive course based on learnerâ??s knowledge. Based on this approach, we developed an adaptive learning system named is ACGS-II, that teaches students how to design an Entity Relationship model in a database system course. Empirical testing results for students who used the application indicate that our proposed model is very helpful as guidelines to develop adaptive learning system to meet learnersâ?? demands.

Serving is the most important hitting technique in tennis, and a good service receiving can instantly reverse the active and passive relationship between serve and receive on the tennis court, and control the rhythm of the court. The purpose of this study is to use an adaptive neuron fuzzy intelligent system to analyze some techniques of tennis serve. In this study, eight male players from the school tennis team were selected as the experimental subjects, whose sports level was above the national tennis level II. Ten weeks before the simulation test, the training time and frequency of 8 subjects were the same. In other words, 5 times a week, 2.5 hours±0.5 hours. The work engineering of adaptive fuzzy system firstly, in the off-line modeling stage, the adaptive fuzzy system uses the rule self splitting technology to generate the initial fuzzy rules, and uses the improved adaptive neural network algorithm to optimize the calculation; then according to the error between the system input and the predicted output, the independent variable is adjusted and replaced; at the same time, the adaptive fuzzy system is further used for calculation In the process of tennis serving, the nonlinear control variables are obtained online and applied to the fuzzy system for control. Next, in the experiment, the system was used to record the body’s movement and service scores during service. The experimental results show that during the service process, the maximum trunk torsion amplitude can reach 48.26 ° and the minimum is only 5.41 ° and the service score accounts for 81.41% and 80.47% of the total scores of the two sections respectively. This shows that the fuzzy system in this study can effectively analyze the service posture and score of athletes. It is concluded that the accurate calculation and analysis of tennis serve by adaptive neuron intelligent fuzzy system in this study is conducive to improve the tennis serviceability and competition performance of players. This research has made a certain contribution to the intellectualization of sports.

These days different e-learning architecture provide different kinds of e-learning experiences due to ―one size fits for all‖ concept. This is no way better than the traditional learning and does not exploit the technological advances. Thus the e-learning system began to evolve to adaptable e-learning systems which adapts or personalizes the learning experience of the learners. Systems infer the characteristics of the learners and identify the preferences of the learners and automatically generate personalized learning path and customize learning contents to the individuals needs. This process is known as adaptation and systems which adapt are known are adaptive systems. So the main objective of this research was to provide an adaptive e-learning system framework which personalizes the learning experience in an efficient way. In this paper a framework for adaptive e-learning system using user response theory is proposed to meet the research objectives identified in section 1.D.

Dengue epidemic is one the hard challenges that Sri Lankan citizen face today. With the fast growth and due to unavailability of medicines, situation has been worsened. The only way to thwart this danger is to extinct the main cause Aedes aegypti mosquito. Current activities to minimize the mosquito population, are done in an ad-hoc manner. This paper proposes a methodology to recognize the patterns of mosquito spread to increase the effectiveness of the national dengue controlling program. Many climate and socio-economic factors such as temperature, precipitation and urbanization are correlated with the dengue spread. By providing those parameters as inputs and records of reported dengue cases as training data to an adaptive fuzzy system, vulnerability of a particular location to dengue can be obtained as the output. Output will estimate 'how dengue is high' as a fuzzy value between 0 and 1. The solution is based on adaptive neuro fuzzy systems and k-means clustering.

In this paper, a novel control scheme adopted from moment control is proposed. In the proposed approach, an adaptive fuzzy system is employed to learn the effective moment. It is easy to see that such an approach can avoid wild guessing for the effective moment, and as shown in our simulation, can have nice control performance. In traditional adaptive fuzzy control approaches, bounds of system functions are required to facilitate supervisory control so as to have the robust control property. It can be expected that when those bounds used in the supervisory controller are not proper, the output may not be able to follow the reference trajectory satisfactorily. With the proposed moment adaptive fuzzy control, the bound needed is only the supremum of the control variance between two consecutive steps. It is much easier to predict. In our study, in order to further relax this requirement, another adaptive system is employed to estimate the residue of the moment adaptive fuzzy control system. It is called residue compensation in this paper. It can be found that with residue compensation, the approach does not need a supervisory controller, but still can quickly track the reference in a satisfactory fashion. Various simulations are conducted to demonstrate the effectiveness of the proposed approaches.

This paper develops a new solution framework for adaptive output feedback fuzzy control systems aimed at effectively dealing with nonlinear systems with multiple input-multiple output (MIMO) delays and in the presence of dynamics and actuator failure uncertainties. Takagi-Sugeno (T-S) fuzzy systems are employed to represent nonlinear systems, which have desired capacity for dynamic system approximation with parametric and structural properties suitable for using rigorous feedback control techniques. A multiple-delay fuzzy system prediction model is derived and its system properties are clarified. Such a prediction model enables the use of a model-based approach for fuzzy control. The design and analysis are presented for an adaptive control scheme for multiple-delay T-S fuzzy systems, as well as for an adaptive actuator failure compensation scheme for systems with redundant actuators subject to uncertain failures, for which new system parametrizations and controller structures are developed. Simulation results are presented to demonstrate the studied new concepts and to verify the desired performance of the new types of adaptive fuzzy control systems.

In this paper the adaptive fuzzy system is used as an adaptive approximator for robot nonlinear dynamics. A theoretical justification for the adaptive approximator is provided by proving that if the representative point (RP or switching function) and its derivative in sliding mode control are used as the inputs of the adaptive fuzzy system, the adaptive fuzzy system can approximate the plant nonlinear dynamics in the neighborhood of the switching hyperplane. Thus the fuzzy controller design is greatly simplified, and at the same time, fuzzy control rules can be obtained easily by the reaching condition due to the sliding mode control. A new method for designing an adaptive fuzzy control system based on sliding mode is proposed for the trajectory tracking control of a robot with unknown nonlinear dynamics. The system stability and tracking error convergence are proved by Lyapunov techniques that yield a novel adaptive parameter learning law.

Fuzzy logic control (FLC) has been applied successfully to many practical problems. Nevertheless, it is viewed with some scepticism, since it does not share the formality of conventional control techniques. It is the purpose of this paper to build on the framework of adaptive fuzzy systems developed by Wang Lin-Xin (1992, 1994), by combining it with sliding mode control (SMC), thereby eliminating some of the limitations of SMC and adaptive fuzzy systems by incorporating the merits of both techniques. Lyapunov's direct method is used to show that an approximate controller can result in a closed-loop system which is stable. The technique developed is applied to a MIMO ROV. >

A practical projective synchronization approach for uncertain chaotic systems with dead-zone input

The research about sliding based approaches is a widely studied topic to the adaptive fuzzy control system designs. In this paper, a novel state error feedback sliding controller is proposed. An optimal feedback gain is required and in this study it is assumed to be unknown. Usually, a rudimentary feedback gain is used. Besides, in order to approximate the state error feedback sliding controller with the optimal feedback gain, an adaptive fuzzy system is employed. Thus, the proposed control scheme consists of an adaptive fuzzy system and a state error feedback sliding controller with a rudimentary feedback gain. In the system framework, the rudimentary state error feedback sliding controller can be viewed as the approximate error estimator of the adaptive fuzzy system. Therefore, such an estimated error can be fed back to the learning of the fuzzy system through a modified adaptive law. With such an approximate error feedback, it is clearly evident from our simulation that the learning speed of the proposed learning scheme is faster than that of the original scheme. Also, with the proposed controller, the system stability not only is guaranteed, but also becomes more stable.

Voltage ranking is an important part of power system security assessment. The commonly used performance index method with a low exponent could suffer from masking effects. This paper proposes a generic compensation factor to reduce the masking problem. An adaptive fuzzy system is used for the calculation of the generic compensation factor. A simplification of the defuzzification process is proposed for improvement of computational efficiency. Convergence analysis shows that values calculated by the adaptive fuzzy system are good approximations. The parameters would only need to be calculated offline once. They can then be applied to various systems for voltage ranking applications. To save computational efforts further, a hybrid strategy is introduced to reduce the number of fuzzy rules. A good ranking method needs an efficient method to derive voltage deviations. Besides the commonly used 1P–1Q calculation method, an alternative method is used for the calculation of voltage deviations. A new version of the ranking assessment diagram is proposed for the presentation of ranking results. Ranking results from two test power systems have been presented to verify the effectiveness of the proposed strategy.

This paper presents an obstacle avoidance method for mobile robot navigation. A fast and effective algorithm is presented for visual tracking and location prediction under dynamic environment. The picture image is first captured and the motion parameters of each object are extracted from the image. A sequence of images are then generated by using the adaptive fuzzy logic system and used to predict the next position and velocity of the moving object. The structure of the adaptive fuzzy logic system is similar to that of the Kaiman filter. The developed one-step ahead motion predictor can be used to predict the profile of the moving target. Monitoring the selected object using visual images, the vision system tracks the objects and adjusts the velocity and direction of the robot to avoid the objects. Using the predicted positions of the objects, an obstacle avoidance technique based on the adaptive fuzzy system is introduced. A hierarchical system structure is suggested to provide a systematic procedure to achieve target-directed navigation and it is implemented step by step integrating each part of the system designed separately. The effects of the individual subsystems are combined to increase the performance of the whole system. Computer simulations are presented for soccer robots in order to demonstrate the feasibility of the proposed algorithm.