Sridharakumar Narasimhan

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The study of gene regulatory networks is a significant problem in systems biology. Of particular interest is the problem of determining the unknown or hidden higher level regulatory signals by using gene expression data from DNA microarray experiments. Several studies in this area have demonstrated the critical aspect of the network structure in tackling(More)
— System Identification is the process of constructing an accurate and reliable dynamic mathematical model of the system from observed data and available knowledge. The choice of inputs used for perturbing the system is critical in the identification and model building exercise. One of the major objectives of system identification is accurate estimation of(More)
In our previous work on " self-optimizing control " we look for simple control policies to implement optimal operation. In particular, we have looked at " what should we control " (choice of controlled variables (CV 's)). For quadratic problems with linear constraints, optimal linear variable combinations c = Hy may be obtained. In this work, we apply these(More)
The computational effort involved in the solution of real-time optimization problems can be very demanding. Hence, simple but effective implementation of optimal policies are attractive. The main idea is to use off-line calculations and analysis to determine the structure and properties of the optimal solution. This will be used to determine alternate(More)
Faults lead to loss of productivity and can eventually lead to loss of human lives. Therefore, fault diagnosis is a critical procedure for increased reliability and safety. Diagnostic observers, especially Unknown Input Observers (UIO) (Frank, 1990), have been well studied in literature. In this paper a novel residual feedback structure is proposed for(More)
— This paper presents a direct adaptive control design to suppress vibrations in nonlinear base-isolated buildings arising due to severe earthquakes. The control design is based on discrete direct adaptive neural control, where the neural controller parameters are adapted using Lyapunov-based tuning laws. There is no explicit identification phase in this(More)