Modeling of a continuous food process with neural networks /
addition to constructing the models, a variety of techniques
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| Format: | Thesis eBook |
| Language: | English |
| Published: |
[Place of publication not identified] :
[publisher not identified] ;
1995.
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| Subjects: | |
| Online Access: | Link to OAKTrust copy |
| Summary: | addition to constructing the models, a variety of techniques basis function (RBF) network, and a time lagged recurrent both next step prediction and multi-step prediction of a constructed. The models were all designed to provide multi- for evaluating neural applications is presented, and a input/multi-output (MIMO) prediction. For next step linear autoregressive model with exogenous inputs (ARX) was methodolgy for applying neural networks for time series modeling is proposed. models were a feedforward sigmoidal network (FFN), a radial multi-step prediction, the TLRNN model performed best. In neural network (TLRNN). As a benchmark for comparison, a prediction, the ARX model provided the best prediction. For snack food continuous frying operation. The three neural Three neural networks were constructed and trained to provide |
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| Item Description: | "Major subject: Agricultural Engineering". Vita. |
| Physical Description: | xii, 154 leaves : illustrations ; 28 cm. Also available online. Issued also on microfiche from Lange Micrographics. |
| Bibliography: | Includes bibliographical references. |