Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce ADAPTIVE GDD (AGDD), which eliminates the inappropriate effect of clustered samples by ADAPTIVEly updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

This abstract serves as a concise yet comprehensive overview of this research's contributions, highlighting its significance in advancing ADAPTIVE Cruise Control technology and autonomous vehicles. The provided paper introduces an innovative approach to ADAPTIVE Cruise Control systems, emphasizing safety, comfort, and efficiency. Also, the proposed ADAPTIVE Cruise Control model surpasses traditional longitudinal velocity control by integrating lateral motion and surface condition considerations. The proposed control STRATEGY uses a new tail-following approach with the implementation of a new throttle valve controller which results in a smoother deceleration with an average of 40 percent decrease in maximum deceleration value while following other vehicles. Also, the implementation of brakes is minimized to lower the overall energy waste in vehicle motion. The proposed ADAPTIVE Cruise Control can regulate braking force and tail-following distance based on road surface material and circumstances. This action enhances safety while driving on various roads and weather conditions. One of the innovative sections in this study is the integration of lateral motion with ADAPTIVE Cruise Control. This approach helps the vehicle to stay laterally stable by limiting the lateral acceleration of the vehicle. The research signifies a notable advancement in ADAPTIVE Cruise Control technology, establishing a connection between vehicle dynamics and ADAPTIVE control algorithms.

In this article, the linear quadratic regulator method (LQR) for voltage control of a linear time-varying model of a robot is used to design an online ADAPTIVE optimal stable controller to trace the robot arm path. Normally, offline solving of Riccati differential equations in backward with final conditions for linear time-varying system or converting the Riccati differential equation to an algebraic one in linear time-invariant system is inevitable in LQR. However, in this paper, the differential Riccati equations are considered as the adaptation laws along with a voltage control STRATEGY to be solved online in forward method with initial conditions. Choosing a proper Lyapunov function guarantees the asymptotic stability of the tracking. Furthermore, parametric model uncertainties such as mass parameter variation and external disturbances which affect the dynamics of the model are also taken into account. Simulation results show the energy used by dc motors of the voltage optimal control STRATEGY is less than that of the torque control STRATEGY and as good as the classical PID one. The superior performance of the voltage optimal control over torque control STRATEGY is also shown in presence of disturbance.

In most practical cases, structural design variables are linked to a discrete list of sections for optimal design. Cardinality of such a discrete search space is governed by the number of alternatives for each member group. The present work offers an ADAPTIVE STRATEGY to detect more efficient alternatives and set aside redundant ones during optimization. In this regard, the difference between the lower and the upper bounds on such variables is gradually reduced by a procedure that adapts history of the selected alternatives in previous iterations. The propsed STRATEGY is implemented on a hybrid paritcle swarm optimizer and imperialist competitive algorithm. The former is a basic swarm intelligent method while the later utilizes subpopulations in its search. Spatial and large-scale structures in various shapes are treated showing successive performance improvement. Variation of a diversity index and resulting band size are traced and discussed to declare behavior merits of the proposed ADAPTIVE band STRATEGY.

his paper presents the problem of distributed estimation in an incremental network based on the family of normalized subband ADAPTIVE algorithms (NSAAs). The distributed NSAA (dNSAA), the distributed selective partial update NSAA (dSPU-NSAA), the distributed dynamic selection NSAA (dDS-NSAA), and the dSPU-DS-NSAA are introduced in a unified way. The dNSAAs have better convergence speed than distributed normalized least mean square (dNLMS) algorithm especially for colored Gaussian input of the nodes. In comparison with dNSAA, the dSPU-NSAA, and dDS-NSAA have lower computational complexity and close performance to dNSAA. Also by combination of these algorithms, the dSPU-DS-NSAA is established which is computationally efficient. In addition, a unified approach for mean-square performance analysis of each individual node is presented. This approach can be used to establish a performance analysis of classical distributed ADAPTIVE algorithms as well. The theoretical expressions for transient, and steady-state performance analysis of the various dNSAAs are introduced. The validity of the theoretical results, and the good performance of these algorithms are demonstrated by several computer simulations.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search STRATEGY has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search STRATEGY to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust ADAPTIVE control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust ADAPTIVE control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

In this paper, an ADAPTIVE load voltage support control STRATEGY is proposed for the inverter to simultaneously control the load voltage, grid current and power injected to the grid in the presence of grid voltage distortions and nonlinearity of the load current. In the proposed control STRATEGY, the local load can be supplied in desired quality of the network operator. In order to employ the proposed control STRATEGY, a cascaded structure has been proposed to control the quality of active injected power to the grid, load voltage and grid current, simultaneously. The proposed controller is able to track and compensate voltage harmonics without need of complex and long-run calculations. The proposed model is simulated under the non-ideal grid conditions while supplying a nonlinear load. The simulation results show the effectiveness of the proposed control STRATEGY to supply a non-linear load in a standard voltage and current.