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Traffic flow prediction plays an important role in urban traffic management and control. Traditional prediction methods are mostly difficult to meet the high complexity, randomness and uncertainty characteristics of urban traffic flow. In this paper, a new prediction model is proposed based on self-adaptive neural network. Compared with other methods, it possesses the advantages of low computational...
A simple and attractive way to reduce the hardware complexity and power consumption of an adaptive filter is to implement it using a partial-update technique whereby only a subset of the adaptive filter coefficients are updated at each iteration. For certain cyclostationary or periodic input signals partial-update techniques become susceptible to divergence problems unlike their full-update counterparts...
The commercial banks risks come from all the uncertainty of the banking business, which have diffusibility and hidden features, if not timely controlled, will have a negative impact on the national economy. Therefore, it is necessary to design the corresponding index system according to the objectivity and relativity of the banking risks, and then control quantitatively the banks risk. Based on the...
State estimation has been favored in many studies. In many control design methods such as state feedback and optimal regulators state variables are needed but very often they are not available at the output of the plant and should be estimated. Also many fault detection and isolation (FDI) techniques use state variables to determine fault kind and location. There exist varies observers for using in...
As inspired by revising (Zhang and Ge, 2003), the traditional gradient-based neural system (also termed analog computer (Manherz et al., 1968)) for matrix inversion is re-visited by examining different activation functions and various implementation errors. A general neural system for matrix inversion is thus presented which can be constructed by using monotonically-increasing odd activation functions...
For complex systems, reinforcement learning has to be generalised from a discrete form to a continuous form due to large state or action spaces. In this paper, the generalisation of reinforcement learning to continuous state space is investigated by using a policy gradient approach. Fuzzy logic is used as a function approximation in the generalisation. To guarantee learning convergence, a policy approximator...
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