Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

“…This was started with two neurons and the number of neurons was increased up to six. The least MSE value and a good prediction of the outputs of both training and validation sets were obtained with four neurons in the hidden layer (Dutta et al, 2004). The R 2 value between the actual and estimated responses was determined as 0.930 (Fig.…”

Interpretation of non-linear empirical data-based process models using global sensitivity analysis

“…Since the model is restricted to the polynomial form, it is not capable of approximating complex factor-response relationship. To address this issue, more flexible data-based models have been adopted, including ANN [6,10], SVM [7] and GP regression [9]. Unlike polynomial regression, these complex models do not allow a transparent interpretation as to which process factors contribute the most to the response variable.…”

“…logarithmic or logistic) transformation is particularly attractive if it is known a priori to result in linear factor-response relationship, and thus linear regression can be used. However in general situation, the prediction accuracy of the polynomial regression is unsatisfactory if the chemical process is complex and does not conform to the restrictive functional form [6,7,8,9]. Consequently, the model-based process understanding and optimization may be unreliable .…”

“…Consequently, the model-based process understanding and optimization may be unreliable . To address this issue, flexible non-linear models have been applied to provide a more accurate approximation of the process behavior, such as artificial neural network (ANN) [6,10], support vector machine (SVM) [7], and Gaussian process (GP) regression 1 [9]. Here "flexible" refers to the property that the models are not restricted to a certain functional form, but capable of approximating any function to any accuracy should sufficient data are available.…”

Optimization of Medium for Protease Production by Enterobacteriaceae sp. PAMC 25617 by Response Surface Methodology

“…다구찌 기법은 직교배열표에 잡음인자라는 개념을 도입한 실험계획법으로 다양한 환경에서도 최적의 성능을 유지할 수 있는 제어인자들의 조합을 확인하는데 주로 이용된다 [20][21][22]. 반응표면분석법은 여러 개의 인자가 복합적인 작용을 함으로써 어떤 반응변수에 영향을 주고 있을 때 반응의 변화가 이루는 반응표 면에 대한 통계적 분석방법으로 독립변수들의 어떠한 값에서 반응 량이 최적화인가를 예측하는데 이용되고 있다 [23][24][25][26][27][28][29][30].…”