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Background The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after...
This study considers offline identification of switched linear MIMO systems using measurements from their inputs and outputs. This is a class of non-convex optimization and ill-posed problems. To convert this optimization into a binary integer programming problem, the proposed approach assumes that the number of switches among the subsystems is upper-bounded. The state-space realization of each sub-system...
Partially connected ANN with Evolvable Topology (PANNET) is a non-fully connected recurrent neural network with proper number of context nodes. The structure of the network along with connection weights are determined through the evolutionary process of a customized genetic algorithm. In this paper, we develop an evolutionary bilevel optimization procedure for tuning the hyper-parameters of PANNET...
Due to the relatively high density of vehicles and humans at intersections, it is crucial for an Advanced Driver Assistance System (ADAS) to predict human driver behaviors to avoid crashes. Due to the complexity of human's behavior interacting with a vehicle, it is very difficult to find an explicit model to analysis the driver's behavior. In this paper Takagi-Sugeno is used as a data driven technique...
This paper aims at identifying switched linear systems, which are described by noisy input/output data. This problem is originally non-convex and ill-posed. The proposed approach utilizes bounded-switching clustering method to convert the problem into a binary integer optimization and least square. This method optimally divides a time series into several clusters whose parameters are piecewise constant...
Reverse Engineering of Gene Regulatory Networks (GRN), i.e. Finding appropriate mathematical models to understand complex cellular systems, can be used in disease diagnosis, treatment, and drug design. There are fundamental gaps in the construction of GRN with regard to modeling of hidden/delayed interactions. Addressing these deficiencies is critical to understanding complex intracellular processes...
Non-stationary time series analysis is important in the study of complex systems. Finding mathematical models for such complex systems with transitions between different phases is an ill-posed problem. This paper brings the problem of time series analysis into the context of hybrid modeling. Approximating the hybrid system by a switched linear system, the problem is reduced to identifying the switching...
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