مقاله انگلیسی رایگان در مورد شبکه عصبی مصنوعی و رگرسیون خطی برای طبقه بندی پایداری سدیم و پتاسیم – اسپرینگر 2019

Majority of conventional prediction methods are based on the assumption of linear dependence of the predicted (dependent) variable and the set of independent descriptors. The multiple linear regression models are built using the correlation and regression analysis methods. Nonlinear relationships between the variables are conventionally modeled via fi tting of nonlinear curves (quadratic, cubic, power, exponential, logarithmic, hyperbolic, or logistic ones) or their linearization. However, the artifi cial neural networks approach has been recognized as promising method to model nonlinear dependences in the prediction tasks over recent decades [1]. Major advantages of the artifi cial neural networks algorithms are their capability to learning, generalization, and prediction of the data, fault tolerance, parallel data processing, and fast computation procedures. This has been supported by the neural networks application in theoretical and computational chemistry, analytical chemistry, biochemistry, medicine, drugs chemistry, pharmaceutics, and food products studies. It should be noted that artifi cial neural networks have been applied to the chemometrics tasks since early 1990ies. Four applications of artifi cial neural networks in chemical engineering have been comprehensively described [2]: fault detection, quality prediction, signal processing, and modeling and control of the processes. Various architectures of artifi cial neural networks and their applications in chemistry have been demonstrated [3], outlining the advantages and disadvantages in comparison with conventional chemometrics methods. A novel approach to prediction of biological activity of peptides and proteins, physics and chemistry-driven artifi cial neural network (Phys-Chem ANN), has been proposed [4]. The Phys-Chem ANN has been based on physical and chemical properties as well as structural features of proteins. The task on classifi cation and prediction of the strength of weak organic acids in aqueous-organic solvents has been solved [5].