Wastewater derived from food production is highly variable, depending on the specific types of food processing operations (e.g., fruit, vegetable, oils, dairy, meat, and fish). Advances in membranes technology have showed many advantages for wastewater treatment of food industry. By implementing membranes, the separated substances and clean water are often recoverable in a chemically unchanged ...

Based on chaotic neural network, a multiple chaotic neural network algorithm combining two different chaotic dynamics sources in each neuron is proposed. With the effect of self-feedback connection and non-linear delay connection weight, the new algorithm can contain more powerful chaotic dynamics to search the solution domain globally in the beginning searching period. By analyzing the dynamic...

We present a new method to compute depth-three threshold circuits for pattern classi cation problems. The rst layer of the circuits is calculated from a sample set S of the classi cation problem by a local search strategy that minimises the error on S for each individual gate. The local search is based on simulated annealing with the logarithmic cooling schedule c(k) = = ln (k + 2). The paramet...

We present an algorithm that computes a depth-three threshold circuit for the classiication of liver tissue. The circuit is calculated from a sample set S of 348 positive (abnormal ndings) and 348 negative (normal liver tissue) examples by a local search strategy. The local search is based on simulated annealing with the logarithmic cooling schedule c(k) = ?= ln (k + 2). The parameter ? depends...

A simulated annealing method for estimating the parameters of Hidden Markov Models is presented. This method is based on the choice of the optimal trajectory of the discrete state. It is applied to both discrete and continuous observations. The program developped needs no speciic initialization of the algorithm by the user, the cooling schedule being general and applicable to any speciic model....

In this paper, we propose a population-based optimization algorithm, Sequential Monte Carlo Simulated Annealing (SMC-SA), for continuous global optimization. SMC-SA incorporates the sequential Monte Carlo method to track the converging sequence of Boltzmann distributions in simulated annealing. We prove an upper bound on the difference between the empirical distribution yielded by SMC-SA and th...

A simulated annealing procedure with acceptance-probability control instead of the usual temperature control is proposed. The algorithm presented here has proved to be fully insensitive to initial parameters values, free of local-minima trapping problems, and shows superior convergence compared to adaptive-step classical simulated annealing with exponential cooling schedule. Experiments on comp...

The simulated annealing algorithm is modified to incorporate a generalization of the Metropolis sampling procedure previously applied to homogenous Markov chains by Hastings[8]. In this report, the inhomogenous version of Hastings' procedure is shown to converge asymptotically under conditions similar to Geman and Geman's logarithmic cooling schedule[7]. The new algorithm is experimentally show...

Using the Matrix-Tree Theorem and coupling methods the convergence of the Parallel Chain (PC) algorithm to the set of global minima is established for various selection functions. It is illustrated that there may be convergence to a set of non-global minima when selecting one of the best states (Best-Wins strategy). In the latter case the convergence of the homogeneous PC algorithm is proved fo...