KEYWORDS: Data modeling, Neural networks, Performance modeling, Frequency modulation, Fermium, Feature extraction, Neurons, Mining, Atomic force microscopy, Internet
With the advent of the era of big data, it takes a lot of time and manpower to build a model that can automatically mine the effective features of data and get the user click-through rate prediction model through training, because the single model classifier needs to extract effective features and input them into the model training by means of feature processing and data mining. In view of the shortcomings of website click-through rate prediction technology, this paper proposes a model based on compressed incentive network to extract the influence of a single factor in the overall project characteristics. The paper also applies the neuron mechanism to extract the effective features of the original features, fuses the features proposed by the two models into new features and adds them to the deep neural network for training. Experiments are designed to prove the rationality of the method. The experimental results show that compared with the current commonly used model, it can improve the AUC index and keep the efficiency within a reasonable range. The research results of this paper are not only of great significance to the development of science and technology, but also affect our daily life and economic consumption all the time.
Machine vision inspection combines a fast and accurate computer with image processing technology, which is of great help to improve the ability of intelligent image recognition. The ultimate goal of the recognition and classification system is to identify the detected defects quickly and accurately. The defects with obvious characteristics can be identified in a short time due to their great differences. Due to the imperfect recognition technology and the low robustness of the algorithm, the detection is inaccurate and the difficulty of the recognition effect is increased. In this paper, based on the targets with similar characteristics, the membership function of the target to be recognized is constructed after extracting the characteristic parameters of the recognized target. The characteristic function of the fuzzy set is established to calculate the membership degree of the target sample belonging to the fuzzy set, and finally, the classification of the characteristic sample is recognized according to the maximum membership principle. The experimental results show that the method of this paper is based on pattern recognition and has good results, which has guiding significance for all kinds of image feature recognition and processing and will greatly reduce the intensity and workload of manual processing.
The heating process of the blast furnace is a complex and huge controlled object, which has the characteristics of nonlinear, multivariable, distributed parameters, fast and slow processes intertwined. It is impossible to achieve satisfactory control results only by traditional control. With the development of artificial intelligence in recent years, neural networks, expert technology, fuzzy and predictive control provide new ideas for the heating control of the blast furnace. In this paper, based on the deep learning framework of artificial intelligence, the LSTM algorithm is proposed. Based on the massive production data, through the model simulation comparison, the results show that the LSTM network modeling has stronger generalization ability, smaller error, and higher accuracy. The LSTM neural network algorithm can effectively deal with the characteristics of time series data, provide guidance for the production practice, and lay a good foundation for the predictive control of blast furnaces.
Microgrid makes full use of distributed energy, reduces energy consumption, and effectively exerts the advantages of distributed generation. Aiming at the problem that the interconnected converter is easily affected by the intermittence and disturbance of new energy sources when operating, the internal electronic devices of the system have small damping inertia and weak immunity. A control strategy with a "power-voltage-current" three-loop structure is designed for the interconnected converter. The active power reference value is generated by the bidirectional droop control, and the damping and inertia of the active power are improved by using the VSG characteristics. At the same time, the small-signal modeling and stability analysis of the "active power-frequency" control based on VSG characteristics is carried out. Through the simulation, the power mutual support between sub-microgrids and the power dynamic balance of the whole system are realized to ensure the stable and reliable operation of the system. Then the feasibility and practicability of the control strategy are verified, which provides a theoretical basis for future research on the coordinated control strategy of the microgrid.
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