The opposite (Opposite Degree, referred to as OD) algorithm is an intelligent algorithm proposed by Yue Xiaoguang et al. Opposite degree algorithm is mainly based on the concept of opposite degree, combined with the idea of design of neural network and genetic algorithm and clustering analysis algorithm. The OD algorithm is divided into two sub algorithms, namely: opposite degree - numerical computation (OD-NC) algorithm and opposite degree - Classification computation (OD-CC) algorithm.
The concept of k-order sequence of first order arithmetic sequence has been defined by mathematical induction based on finite difference theory. It has been proved this sequence is higher arithmetic sequence. Meanwhile the sum formula and the derivation of its implicit common difference have been given.
In order to achieve the goal of medical education, medicine and adapt to changes in the way doctors work, with the rapid medical teaching methods of modern science and technology must be reformed. Based on the current status of teaching in medical colleges method to analyze the formation and development of medical teaching methods, characteristics, about how to achieve optimal medical teaching methods for medical education teachers and management workers comprehensive and thorough change teaching ideas and teaching concepts provide a theoretical basis.
The shortage of energy resources is a serious problem our country is facing the world today, petrochemical enterprises are the main pillar of our economy, in our country, because petrochemical companies accounted for about 30.2% of the total energy consumption of energy. This paper focuses on the traditional energy-saving lighting methods proposed intelligent energy-saving system based on a fuzzy control. The purpose is to make the electrical equipment to fully meet, under the premise of improving its functional requirements, as far as possible to reduce energy consumption and improve energy efficiency.
The software reliability is the ability of the software to perform its required function under stated conditions for a stated period of time. In this paper, a hybrid methodology that combines both BP neural network and fractal models is proposed to take advantage of unique strength of BP neural network and fractal in modeling. Based on the experiments performed on the software reliability data obtained from literatures, it is observed that our method is effective through comparison with past methods and a new idea for the research of the software failure mechanism is presented.
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