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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… (More)

Computational prediction of protein function is an important field in functional genomics. Gene function prediction is a Hierarchical Multi Label Classification (HMC) problem where each gene can belong to more than one functional class simultaneously, while classes are structured in the form of hierarchy. HMC is becoming a necessity in many domains of… (More)

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… (More)

In nonlinear and chaotic time series prediction, constructing the mathematical model of the system dynamics is not an easy task. Partially connected Artificial Neural Network with Evolvable Topology (PANNET) is a new paradigm for prediction of chaotic time series without access to the dynamics and essential memory depth of the system. Evolvable topology of… (More)

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… (More)

- Mina Moradi Kordmahalleh, Mohammad Gorji Sefidmazgi, Abdollah Homaifar
- 2015 IEEE 14th International Conference on…
- 2015

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… (More)

Time series analysis is an important research topic in science and engineering. Real-world time series are usually non-stationary with time-varying parameters. Identification of non-stationary time series with a switched model includes finding the switch times and model parameters in each cluster. This problem is a non-convex optimization with equality… (More)

Hurricanes constitute major natural disasters that lead to destruction and loss of lives. Therefore, to reduce economic loss and to save human lives, an accurate forecast of hurricane occurrences is crucial. Despite the availability of data and advanced forecasting techniques, there is a need for effective methods with higher accuracy of prediction. We… (More)

Wireless Sensor Networks (WSNs) consist of many low cost and light sensors dispersed in an area to monitor the physical environment. Event detection in WSN area, especially detection of multi-events at the same time, is an important problem. This article is a new attempt for detection of two simultaneous events based on distributed data processing structure… (More)

A proposed sparse recurrent neural network with flexible topology is used for trajectory prediction of the Atlantic hurricanes. For prediction of the future trajectories of a target hurricane, the most similar hurricanes to the target hurricane are found by comparing directions of the hurricanes. Then, the first and second differences of their positions… (More)