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To solve the problem of training rate decline in neural network caused by too much noise in the traditional image, a new method of expression recognition based on CNN was proposed. First, in order to narrow the face range, face image could be detected from the original image by using the AdaBoost cascade classifier. Then, the coordinates of the eye, mouth and other key parts and brow, nasolabial and...
Vehicular density control on the motorway mainlines is a complicated problem and traditional control approaches of ramp metering prove to be unsatisfactory. In this paper, a composite control method based on credit assignment cerebellar model articulation controller (CACMAC) and proportional-integral-derivative (PID) controller is applied to highway density control. Firstly, a macroscopic traffic...
This paper investigates the state estimation of fractional-order neural networks (FNNs) with time delay. This is the first to study the state estimation for delayed fractional-order nonlinear system. According to fractional-order Lyapunov direct approach together with linear matrix inequalities (LMIs), sufficient criteria are given to ensure the asymptotical stability of the estimation error system...
Recently, deep learning has been proposed and verified to possess the strong ability to learn and express complex features, which has brought significant research achievements in signal processing. As a challenging task in speech signal processing, monaural speech separation has always been the research focus of researchers. From the usage of traditional signal processing methods and shallow models...
A concatenated LSTM (Long Short-Term Memory) architecture for CHP (combined heat and power) heat load forecasting was presented. Firstly, input data was normalized and separated into historical climate and heat load data. Then feed the separated data into two LSTM neural networks. Finally, the two LSTM models were concatenated as inputs to another LSTM model followed by two dense layers. Relu function...
Use flue gas wet desulphurization technology to reduce the emissions of sulfur dioxide. The improved BP neural network model based on LM algorithm was established according to the factors that influence sulfur dioxide emissions, and the network weights and threshold value were adjusted repeatedly. The real data obtained from thermal power plants are simulated and verified. The results show that the...
Human action recognition is one of the most active research areas of computer vision. With the rapid development of deep learning, using neural networks to realize action recognition becomes a popular thesis. This paper proposes a self-learned action recognition method based on neural networks. The proposed method trains dictionaries with sparse autoencoder (SAE) and extracts the key frames with sparse...
The adaptive neural backstepping control for a class of multiple-input and multiple-output nonlinear systems with quantized input signals is studied in this paper. We design an output feedback adaptive control scheme using backstepping method based on a high-gain state observer, and use neural networks to approximate the unknown nonlinear functions. A new output feedback neural controller is proposed...
The photoelectric conversion efficiency of photovoltaic cells is mainly affected by two factors, two factors are the operating temperature of the photovoltaic cell and the irradiance of the sun. In order to improve the photoelectric conversion efficiency of photovoltaic cells, combining with the two factors that affect photoelectric conversion efficiency of photovoltaic cells and the merits and demerits...
Most present methods of saliency detection emphasize too much on the local contrast while ignore the global feature of image. The detailed characteristics of the image can be reflected based on the local comparison of image. However, the overall saliency of the image cannot be reflected. In this paper, a saliency detection model combined local and global features was proposed. Firstly, a local feature...
In cellular mobile wireless networks, nonlinear channel fading is always a critical factor which will scientifically deteriorate the Quality of Service (QoS) for the users. However, it is difficult to describe the nonlinear channel fading by precise mathematical model because of its complexity. So, the control of wireless networks becomes an especial tough work. In this paper, a power and rate control...
This paper investigates the problem of finite-time extended dissipative analysis for a class of switched delay systems. By using average dwell-time and linear matrix inequality technique, sufficient conditions are proposed to guarantee the switched delay system is finite-time bounded and has finitetime extended dissipative performance, where the H∞, L2 — L∞, Passivity and (Q, S, Ä)-dissipativity performance...
In this paper, finite-time synchronization for delayed complex-valued bidirectional associative memory (BAM) neural networks is investigated. Based on the finite-time stability and some inequality techniques, a synchronization criterion is provided to guarantee that the drive-response systems achieve synchronization in finite time, and the settling time can be estimated effectively. Meanwhile, a suitable...
Aim to the problem of engine fault diagnostic accuracy, research an engine fault diagnosis technologies based on adaptive neuro-fuzzy inference system. The method is proposed that using known input and output data, construct a fuzzy inference system, internal systems adjust the membership function using a least squares method based on hybrid learning algorithm and reverse the spread of the combination...
This paper is concerned with the exponential lag anti-synchronization for a class of memristive neural networks with multiple time delays. By utilizing discontinuous control theory, several new sufficient conditions concerning exponential lag anti-synchronization are obtained. The obtained results complement and extend earlier publications on memristive or conventional neural networks. Finally, numerical...
Most traditional soft sensor modeling requires the labeled training samples that contain both subsidiary and key variables. However, key variables are difficult to be obtained online due to lack of detection information or high measurement cost. In this paper, a novel semi-supervised learning algorithm, called cotraining-style kernel extreme learning machine, is proposed to exploit unlabeled training...
The influence of temperature, irradiance and shielding ratio on the output characteristic curve of photovoltaic cells was studied in this paper. In order to improve the photoelectric conversion efficiency of photovoltaic cells, combining three major factors that affect photovoltaic cells, a maximum power point tracking (MPPT) scheme based on large variation genetic algorithm was proposed. In this...
This paper proposes a neural network controller to achieve the tracking of a robot manipulator with a highly nonlinear structure, and presents a online adaptive control algorithm. The controller based on RBF neural network approximation is designed, and its stability and convergence is analyzed under four different circumstances. By minimizing the system error and considering the characteristic of...
To solve the problem of low recognition rate which is the existing identification methods of partial discharge faults, a new method was designed with wavelet, singular value and improved particle swarm algorithm to optimize the BP neural network. First, using continuous wavelet and singular value decomposition to get the signal characteristic value; then combined with the significance of inertia weight...
Traditional pairwise learning to rank algorithms pay little attention to top ranked documents in the query list, and do not work well when they are used on a data set with multiple rating grades. In this paper, a novel pairwise learning to rank algorithm is proposed to solve this problem. This algorithm defines a bounded loss function and introduces the preference weights between document pairs into...
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