Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos
In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised ne...
Na minha lista:
| Autor principal: | |
|---|---|
| Outros Autores: | |
| Formato: | doctoralThesis |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
|
| Assuntos: | |
| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/13866 |
| Tags: |
Adicionar Tag
Sem tags, seja o primeiro a adicionar uma tag!
|
| Resumo: | In this paper artificial neural network (ANN) based on supervised and unsupervised
algorithms were investigated for use in the study of rheological parameters of solid
pharmaceutical excipients, in order to develop computational tools for manufacturing solid
dosage forms. Among four supervised neural networks investigated, the best learning
performance was achieved by a feedfoward multilayer perceptron whose architectures was
composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one
neuron in the output layer. Learning and predictive performance relative to repose angle was
poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good
fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the
next stage of development of supervised ANNs. Clustering capacity was evaluated for five
unsupervised strategies. Network based on purely unsupervised competitive strategies, classic
"Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize
Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform
clustering from database, however this classification was very poor, showing severe
classification errors by grouping data with conflicting properties into the same cluster or even
the same neuron. On the other hand it could not be established what was the criteria adopted
by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas
(NG) networks showed better clustering capacity. Both have recognized the two major
groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM
showed some errors in classify data from minority excipients, magnesium stearate (EMG) ,
talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent
classification of data and solve the misclassification of SOM, being the most appropriate
network for classifying data of the study. The use of NG network in pharmaceutical
technology was still unpublished. NG therefore has great potential for use in the development
of software for use in automated classification systems of pharmaceutical powders and as a
new tool for mining and clustering data in drug development |
|---|