Use of Decision Tree Analysis for Determination of Critical Enological and Viticultural Processing Parameters in Historical Databases

Subramanian, Venkatanarayanan; Block, David E.

doi:10.5344/ajev.2001.52.3.175

Am J Enol Vitic.

2001

DOI: 10.5344/ajev.2001.52.3.175

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Use of Decision Tree Analysis for Determination of Critical Enological and Viticultural Processing Parameters in Historical Databases

Venkatanarayanan Subramanian¹,

David E. Block²

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Cited by 11 publications

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Nonlinear experimental design using Bayesian regularized neural networks

Block

2007

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).Novel criteria for designing experiments for nonlinear processes are presented. These criteria improve on a previous methodology in that they can be used to suggest a batch of new experiments to perform (as opposed to a single new experiment) and are also optimized for discovering improved optima of the system response. This is accomplished by using information theoretic criterion, which also heuristically penalize experiments that are likely to result in low (nonoptimal) results. While the methods may be applied to any type of nonlinear-nonparametric model (radial basis functions and generalized linear regression), they are here exclusively considered in conjunction with Bayesian regularized feedforward neural networks. A focus on the application of rapid process development, and how to use repeated experiments to optimize the training procedures of Bayesian regularized neural networks is shown. The presented methods are applied to three case studies. The first two case studies involve simulations of one and two-dimensional (2-D) nonlinear regression problems. The third case study involves real historical data from bench-scale fermentations generated in our laboratory. It is shown that using the presented criteria to design new experiments can greatly increase a feedforward neural network's ability to predict global optima.

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Nonlinear experimental design using Bayesian regularized neural networks

Block

2007

AIChE Journal

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show abstract

New experimental design method for highly nonlinear and dimensional processes

Zhang¹,

Olsen²,

Block³

2007

AIChE Journal

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in Wiley InterScience (www.interscience.wiley.com).A novel nonlinear experimental design scheme to identify the optimum of a multidimensional system was computationally evaluated. The method, a hybrid neural networkgenetic algorithm coupled with a fuzzy clustering technique, was developed to accumulate former experimental knowledge and suggest optimal operating conditions for future experimentation. Because of the uncertainty associated with complex systems, several response surfaces with various degrees of complexity and dimensionality were used as case studies for evaluating the new methodology to gain insight into the challenges that may be presented by arbitrary experimental optimization problems in practice. The long-term objective of this work is to develop an experimental design scheme that will discover the best process operating conditions for any difficult process optimization problem in as few experiments as possible. The simulation results demonstrate that the algorithm performance is not significantly affected by parameters such as the number of experiments in a batch of experimentation and convergence criteria for the genetic algorithm as well as for various noise levels below 15%. However, higher noise levels, complexity, and dimensionality decrease its performance. Even at higher levels of noise, complexity, and dimensionality, though, the new approach finds significantly better solutions than those found with traditional statistical experimental design methods, with similar or only slightly more experiments.

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Integration of data mining into a nonlinear experimental design approach for improved performance

Zhang

Block

2009

AIChE Journal

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Use of Decision Tree Analysis for Determination of Critical Enological and Viticultural Processing Parameters in Historical Databases

Cited by 11 publications

References 0 publications

Nonlinear experimental design using Bayesian regularized neural networks

Nonlinear experimental design using Bayesian regularized neural networks

New experimental design method for highly nonlinear and dimensional processes

Integration of data mining into a nonlinear experimental design approach for improved performance

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