Deep Pile Foundation Settlement Prediction Using Neurofuzzy Networks

A NeuroFuzzy System (NFS) is one of the most commonly used systems in the real life problems because it has explicit and transparency which results from the fuzzy systems, with the learning and generalization capabilities from the dynamic behavior of the neural networks. It is one of the most successful systems, which introduced to decrement the fuzzy rules that constituting the underlying model. This system has a high efficiency; it gives good results in high speed. The NFS used in this study to predict the settlement of deep pile foundations. The results obtained from this system give good agreement and high precious for prediction of settlement compared with hyperbolic model and statistical regression analysis. Also, this scenario can be applied for similar or more complicated problems in the geotechnical engineering.

Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction

Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.

USING NEUROFUZZY NETWORKS TO MIMIC ANESTHESIOLOGIST KNOWLEDGE IN DECISION MAKING ON PROPOFOL ADMINISTRATION

The purpose of anesthesia is to maintain a steady state for specific clinical operations. In general, one anesthesiologist utilizes anesthetic drugs and anesthetic skills to make sure the depth of anesthesia (DOA) carefully in proper level such that a patient will not perceive pain during surgical procedure. It is complex to be treated as an art to reduce all sensations, whether it is the sense of pain, touch, temperature, or position. In this paper, utilizing the self-learning and the human-like reasoning ability of neurofuzzy networks, we design the virtual anesthesiologist to accommodate the knowledge and the experience of the real anesthesiologist in anesthetic drug administration. The heart rate and bispectral index are used as the input variables and the bispectral index target value (BIStarget) heart is treated as output variable. The anesthesia simulator is adopted to verify the virtual anesthesiologist's ability and to explore the patient status of the simulator. The pilot experiments and extended experiments have been carried out. The result showed that the virtual anesthesiologist was able to support the decision making on the maintenance of the patient DOA at BIStarget 60.