Adaptive Network Framework Research Articles

Control of D-Statcom in Appropriation Framework Involving ANFIS for Further Developing Power Quality

This paper introduces an intelligent controller designed for the optimal operation of a D-STATCOM (Distribution Static Compensator) utilizing the ANFIS. In primary focus is improve the power quality within a 3 phase, four wire system. And distribution of fortifying and overall consistency. Then the ANFIS controller in-corporates fuzzy logic’s, devices are applied in “adaptive network framework”. To use the ANFIS method are established and using the MATLAB Simulink software. D-STATCOM, featuring a VSI and a DC side capacitance, excels in reactive power compensation and harmonic mitigation, even when subjected to dynamic load conditions and advice of the DC link capacitor V, the proposed ANFIS controller generates a compensation current reference. This reference adequately addresses the reactive power requirements imposed by nonlinear loads, ensuring the maintenance of DC voltage regulation. Through comprehensive simulation results, it is evident that the ANFIS controller surpasses in providing superior harmonic compensation and robust DC link voltage regulation under varying operating conditions. This research contributes to the advancement of intelligent control strategies for enhancing power quality and reliability in distribution systems.

Balancing Quarantine and Self-Distancing Measures in Adaptive Epidemic Networks

We study the relative importance of two key control measures for epidemic spreading: endogenous social self-distancing and exogenous imposed quarantine. We use the framework of adaptive networks, moment-closure, and ordinary differential equations to introduce new model types of susceptible-infected-recovered (SIR) dynamics. First, we compare computationally expensive, adaptive network simulations with their corresponding computationally efficient ODE equivalents and find excellent agreement. Second, we discover that there exists a critical curve in parameter space for the epidemic threshold, which suggests a mutual compensation effect between the two mitigation strategies: as long as social distancing and quarantine measures are both sufficiently strong, large outbreaks are prevented. Third, we study the total number of infected and the maximum peak during large outbreaks using a combination of analytical estimates and numerical simulations. Also for large outbreaks we find a similar compensation mechanism as for the epidemic threshold. This means that if there is little incentive for social distancing in a population, drastic quarantining is required, and vice versa. Both pure scenarios are unrealistic in practice. The new models show that only a combination of measures is likely to succeed to control epidemic spreading. Fourth, we analytically compute an upper bound for the total number of infected on adaptive networks, using integral estimates in combination with a moment-closure approximation on the level of an observable. Our method allows us to elegantly and quickly check and cross-validate various conjectures about the relevance of different network control measures. In this sense it becomes possible to adapt also other models rapidly to new epidemic challenges.

Network Structure and Selection Asymmetry Drive Coevolution in Species-Rich Antagonistic Interactions.

Ecological interactions shape and are shaped by the evolution of interacting species. Mathematical models and empirical work have explored the multiple ways coevolution could occur in small sets of species, revealing that the addition of even one species can change the coevolutionary dynamics of a pairwise interaction. As a consequence, one of the current challenges in evolutionary biology is to understand how species-rich assemblages evolve and coevolve as networks of interacting species. We combined an adaptive network framework, a trait evolutionary model, and data on network structure to study how network organization affects and is affected by selection in antagonistic interactions such as parasitism, predation, and herbivory. We explored how selection imposed by interactions shapes the evolution of attack and defense traits, parameterizing our models with structural information from 31 empirical assemblages of antagonistic species. In the simulations, the form of coevolution in antagonistic interactions is affected by the intensity and asymmetry of the selection imposed by the interacting partners. Transient escalation in attack and defensive traits was the most prevalent form of coevolutionary dynamics, especially in networks formed by modules of highly interacting species. Fluctuating evolution of traits was observed when the intensity of selection was higher in exploiters than in victims and was especially favored in nested networks. At the species level, highly connected species experienced higher temporal variation in selection regardless of the network structure, resulting in high trait mismatching with their partners. The mismatched patterns of highly connected species, in turn, may explain the emergence of modularity in antagonistic interactions in which selection is stronger on exploiters than on their victims. Our results highlight the roles of different aspects of network structure on antagonistic coevolution: nestedness shapes coevolutionary dynamics, whereas modularity emerges as one outcome of coevolutionary dynamics.

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

The Muskingum flood routing model using a neuro-fuzzy approach

The study presents the combined application of Fuzzy Inference System (FIS) and Muskingum model in flood routing. The rules of FIS are incorporated with the Muskingum formula and the model is called the Muskingum FIS model in the study. The proposed model estimates the outflow by applying a Network-based Fuzzy Inference System (ANFIS), which is a FIS implemented in the adaptive network framework. Simulation results indicate that the proposed scheme is an advisable approach for the flood routing. Case study is presented to demonstrate that the FIS is an alternative in application of the Muskingum model.

Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design

Obtaining optimal solutions for time-varying groundwater remediation design is a challenging task. A novel procedure first employs input/output data sets obtained by constrained differential dynamic programming (CDDP). Then the Adaptive-Network-Based Fuzzy Inference System (ANFIS), which is a fuzzy inference system (FIS) implemented in the adaptive network framework, is applied to acquire time-varying pumping rates. Results demonstrate that the FIS is an efficient way of groundwater remediation design.

Hardware Implementation of an Adaptive Network-Based Fuzzy Controller for DC–DC Converters

Two control topologies of adaptive network-based fuzzy inference system (ANFIS) for control of the DC-DC converter are developed and presented in this paper. They essentially consist of combining fuzzy inference system and neural networks and implementing within the framework of adaptive networks. The architecture of the ANFIS along with the learning rule, which is used to give an adaptive and learning structure to a fuzzy controller, is also described. The emphasis here is on fuzzy-neural-network control philosophies in designing a novel controller for the DC-DC converter that allows the benefits of neural network structure to be realized without sacrificing the intuitive nature of fuzzy system. Specifically, it permits this type of setup to simultaneously share the benefits of both fuzzy control and neural network capabilities. Furthermore, the membership functions and rules can be retrieved from the ANFIS after training. A commensurate number of components is designed and built. The components are tested individually and in various combinations of hardware and software segments. Two categories of tests, namely, load regulation, and line regulation will be carried out to evaluate the performance of the proposed control system. Test results demonstrate the advantages and flexibilities of the ANFIS for the DC-DC converter.

Neuro-fuzzy modeling and control

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. >

ANFIS: adaptive-network-based fuzzy inference system

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>