Abstract
A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.
Highlights
A moment of evolution is emerging toward a new paradigm known as smart convergence, which is bringing together both heterogeneous and information communication technologies.These emerging phenomena have prompted researchers to explore new possibilities for sophisticated smart devices [1] to be embedded in various real objects and to cope with various environmental changes
Given that the accuracy of a sensor system is dictated by the degree to which repeated measurements under unchanging conditions are able to produce the same results, a multi-sensor system has typically been thought of as a way to guarantee the accuracy of a measurement system [5]
Multi-sensor systems are an emerging research topic that is becoming increasingly important in various environmental perception activities
Summary
A moment of evolution is emerging toward a new paradigm known as smart convergence, which is bringing together both heterogeneous and information communication technologies. Venu et al [18] showed that the parameters of feed-forward neural network converge faster using other algorithm based on back-propagation methods, e.g., stochastic gradient descent, scaled. A multi-sensor system represents proven method for enhancing accuracy the a data fusion model. Each output of the neural network the hidden units described in Equation (2): as p = [ p1 p2 ] T calculates the activation function based on the hidden units described in Equation (2):. Kim et al [16] suggested a PSO-based neural network model, namely PSO, which can be used as alternatives method to the gradient-descent algorithm by randomly spreading multiple particles capable of finding each optimum and being converged toward the global optima. If a system needs many parameters that should be adjusted for many epochs and other potential parameters, the system needs to be improved
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