A hybrid method based on modified discrete artificial bee colony algorithm(MDABC) for power quality disturbance(PQD) signal feature selection and parameter optimization of random forest(RF) is proposed. Firstly, time-frequency features of the complex power quality disturbance signal are extracted using s-transform(ST) to form an original feature set; Then, by using default parameters of RF, the out-of-bag(OOB) Permutation test value of each feature in the original feature set is calculated as the feature weight, and the features of the training and verification data set are rearranged in descending order accordingly. Finally, taking the generalization error of RF as the objective function, the forest scale: nTree, the number of input features:Ni and the node feature subset size:q are optimized using MDABC to determine the optimal parameters of RF and the optimal feature set. It can be seen from the experiment that compared with the RF classifier before optimization, the accuracy of the MDABC-RF classifier in the classification of 16 and 19 complex PQD signals is better, and its operating efficiency is greatly improved.
In multi-agent system (MAS), the communication topology of agent network plays a very important role in its collaboration. Small-world networks are the networks with high local clustering and small average path length, and the communication networks of MAS can be analyzed within the frame of small-world topology. Yet the real multiagent communication networks are abundant and the classical WS small-world model is not suitable for all cases. In this paper, two new small-world network models are presented. One is based on random graph substrate and local nodes preference reconnection and the other is based on regular graph substrate and long-range nodes preference reconnection. The characteristic of the network parameter such as the clustering coefficients, average path length, and eigenvalue λ 2 and λ n of the Laplacian matrix for these two models and WS model is studied. The consensus problem that based on these three models is also studied. An example is given and the conclusions are made in the end.
In this paper, stabilization problems for a particular class of non linear continuous model with time-delays in the form of Takagi-Sugeno(TS) are investigated. The considered models are of multiple time-delays in state. Some new sufficient conditions for stability and stabilizability are given. They are based on some PDC control laws and some matrix translation properties. First of all, we derive stability conditions of unforced and forced system of considering model, respectively. We then discuss stabilization problem with two cases: (i) unknown delays and (ii) known delays, and give stabilization conditions via LMI. All given conditions are in the form of LMI's and it's easy to be checked by means of MATLAB. Finally, two examples are worked out to illustrate the theory.
This paper presents an adaptive control method for a class of nonlinear systems with matched uncertainties. Firstly, radial basis function neural networks is adopted to approximate the unknown system perturbance, then an robust adaptive control law is developed to stabilize the system based on the so-called integral sliding mode design approach. The reachability of the sliding surface and the convergence of the weight of the neural networks are showed by Lyapunov theory. Finally, some simulation studies are included to illustrate the effectiveness of the proposed method.
Accurate wind speed forecasting exerts a critical role in energy conversion and management of wind power. In term of this purpose, a hybrid model based on multi-stage principal component extraction, kernel extreme learning machine (KELM) and gated recurrent unit (GRU) network is developed in this paper, where the multi-stage principal component extraction combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), singular spectrum analysis (SSA) and phase space reconstruction (PSR). Firstly, CEEMDAN is employed to decompose the raw wind speed data into a sequence of intrinsic mode functions (IMFs) and a residual component. Then the principal components and residual components of all IMFs are captured by SSA. Meanwhile, all residual components obtained by CEEMDAN decomposition and SSA processing are added to form a new component. Subsequently, PSR is utilized to construct each forecasting component obtained by CEEMDAN-SSA into the input and output of training set and testing set for the prediction model. Later, KELM and GRU neural network are conducted to predict the high-frequency and low-frequency components, respectively. Eventually, the prediction values of each component are accumulated to acquire the final prediction result. To evaluate the performance of the proposed model, four datasets from Sotavento Galicia wind farm are adopted to conduct experimental research. The experimental results manifest that the proposed model achieves higher accuracy of multi-step prediction than other comparative models.
In this paper, we mainly investigate the character of consensus convergence speed of Multi-Agent Systems (MAS) with small-world communication network and the method of devising a speed-optimized small-world communication network in consensus problem based on genetic-algorithm (GA). It is found that, for a small-world communication network, the time to reach a consensus changes rapidly with the change of the number of long-range communication links and the agents which the long-range links connect. The convergence speed of consensus of MAS increases rapidly with the number of long-range links increasing. As we construct a small-world communication network for MAS with a smaller network size and fixed long-range links, we can optimize the long-range link configuration using GA methodology to obtain a small-world communication network with faster consensus speed for MAS.
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