Biomedical subjects

Daming Shi

Publications and source records attributed to Daming Shi.

4 recordsLinked to original sources

PubMed2006

FCMAC-BYY: fuzzy CMAC using Bayesian Ying-Yang learning.

As an associative memory neural network model, the cerebellar model articulation controller (CMAC) has attractive properties of fast learning and simple computation, but its rigid structure makes it difficult to approximate certain functions. This research attempts to construct a novel neural fuzzy CMAC, in which Bayesian Ying-Yang (BYY) learning is introduced to determine the optimal fuzzy sets, and a truth-value restriction inference scheme is subsequently employed to derive the truth values of the rule weights of implication rules. The BYY is motivated from the famous Chinese ancient Ying-Yang philosophy: everything in the universe can be viewed as a product of a constant conflict between opposites-Ying and Yang, a perfect status is reached when Ying and Yang achieve harmony. The proposed fuzzy CMAC (FCMAC)-BYY enjoys the following advantages. First, it has a higher generalization ability because the fuzzy rule sets are systematically optimized by BYY; second, it reduces the memory requirement of the network by a significant degree as compared to the original CMAC; and third, it provides an intuitive fuzzy logic reasoning and has clear semantic meanings. The experimental results on some benchmark datasets show that the proposed FCMAC-BYY outperforms the existing representative techniques in the research literature.

Algorithms↗

PubMed2005

Application of multidimensional scaling to preformulation sciences: a discriminatory tool to group microcrystalline celluloses.

Pre-formulation studies constitute the first step of any pharmaceutical product development and manufacture. Establishment of a comprehensive library of critical physical, chemical, biological and mechanical properties of all materials used for a formulation can be costly, tedious and time consuming, despite its importance in quality manufacturing management. This study seeks to demonstrate the pharmaceutical application of multidimensional scaling (MDS) by incorporating it as a pre-formulation tool for grouping an expanded range of microcrystalline celluloses (MCC). MDS presents the various MCC grades in two-dimensional space based on their torque rheological properties; thus conferring an extra dimension to the pre-formulation tool to facilitate the visualization of the relative positions of each MCC grade. Through this work, the utility of MDS for expediting pre-formulation studies, in particular, grouping of excipients that are available in different brands and grades can be amply exemplified.

Cellulose↗

PubMed2004

A novel preformulation tool to group microcrystalline celluloses using artificial neural network and data clustering.

PURPOSE: To group microcrystalline celluloses (MCCs) using a combination of artificial neural network (ANN) and data clustering. METHODS: Radial basis function (RBF) network was used to model the torque measurements of the various MCCs. Output from the RBF network was used to group the MCCs using a data clustering technique known as discrete incremental clustering (DIC). Rheological or torque profiles of various MCCs at different combinations of mixing time and water:MCC ratios were obtained using mixer torque rheometry (MTR). Correlation analysis was performed on the derived torque parameter Torque(max) and physical properties of the MCCs. RESULTS: Depending on the leniency of the predefined threshold parameters, the 11 MCCs can be assigned into 2 or 3 groups. Grouping results were also able to identify bulk and tapped densities as major factors governing water-MCC interaction. MCCs differed in their water retentive capacities whereby the denser Avicel PH 301 and PH 302 were more sensitive to the added water. CONCLUSIONS: An objective grouping of MCCs can be achieved with a combination of ANN and DIC. This aids in the preliminary assessment of new or unknown MCCs. Key properties that control the performance of MCCs in their interactions with water can be discovered.

Cellulose↗

PubMed2003

Entropy learning and relevance criteria for neural network pruning.

In this paper, entropy is a term used in the learning phase of a neural network. As learning progresses, more hidden nodes get into saturation. The early creation of such hidden nodes may impair generalisation. Hence an entropy approach is proposed to dampen the early creation of such nodes by using a new computation called entropy cycle. Entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes. At the end of learning, the less important nodes can then be pruned to reduce the memory requirements of the neural network.

Algorithms↗