Robust blind deconvolution algorithm: variance approximation and series decoupling

An alternative blind deconvolution algorithm for white-noise driven minimum phase systems is presented and verified by computer simulation. This algorithm uses a cost function based on a novel idea: variance approximation and series decoupling (VASD), and suggests that not all autocorrelation function values are necessary to implement blind deconvolution.

Simple adaptive momentum: new algorithm for training multilayer perceptrons

The speed of convergence while training is an important consideration in the use of neural nets. The authors outline a new training algorithm which reduces both the number of iterations and training time required for convergence of multilayer perceptrons, compared to standard back-propagation and conjugate gradient descent algorithms.

Improving mW/MHz ratio in FPGAs pipelined designs

This paper presents a simple clocking technique to migrate classical synchronous pipelined designs to a synchronous functional-equivalent alternative system in the context of FPGAs. When the new pipelined design runs at the same throughput of the original design, around 30% better mW/MHz ratio was observed in Virtex-based FPGA circuits. The evaluation is done using a simple but representative and practical systolic design as an example. The technique in essence is a simple replacement of the clocking mechanism for the pipe-storage elements; however no extra design effort is needed. The results show that the proposed technique allows immediate power and area-time savings of existing designs rather than exploring potential benefits by a new logic design to the problem using the classic pipeline clocking mechanism.

The effect of test meal monounsaturated fatty acid: saturated fatty acid ratio on postprandial lipid metabolism

Epidemiological evidence shows that a diet high in monounsaturated fatty acids (MUFA) but low in saturated fatty acids (SFA) is associated with reduced risk of CHD. The hypocholesterolaemic effect of MUFA is known but there has been little research on the effect of test meal MUFA and SFA composition on postprandial lipid metabolism. The present study investigated the effect of meals containing different proportions of MUFA and SFA on postprandial triacylglycerol and non-esterified fatty acid (NEFA) metabolism. Thirty healthy male volunteers consumed three meals containing equal amounts of fat (40 g), but different proportions of MUFA (12, 17 and 24% energy) in random order. Postprandial plasma triacylglycerol, apolipoprotein B-48, cholesterol, HDL-cholesterol, glucose and insulin concentrations and lipoprotein lipase (EC 3.1.1.34) activity were not significantly different following the three meals which varied in their levels of SFA and MUFA. There was a significant difference in the postprandial NEFA response between meals. The incremental area under the curve of postprandial plasma NEFA concentrations was significantly (P = 0.03) lower following the high-MUFA meal. Regression analysis showed that the non-significant difference in fasting NEFA concentrations was the most important factor determining difference between meals, and that the test meal MUFA content had only a minor effect. In conclusion, varying the levels of MUFA and SFA in test meals has little or no effect on postprandial lipid metabolism.

An LMS style variable tap-length algorithm for structure adaptation

Searching for the optimum tap-length that best balances the complexity and steady-state performance of an adaptive filter has attracted attention recently. Among existing algorithms that can be found in the literature, two of which, namely the segmented filter (SF) and gradient descent (GD) algorithms, are of particular interest as they can search for the optimum tap-length quickly. In this paper, at first, we carefully compare the SF and GD algorithms and show that the two algorithms are equivalent in performance under some constraints, but each has advantages/disadvantages relative to the other. Then, we propose an improved variable tap-length algorithm using the concept of the pseudo fractional tap-length (FT). Updating the tap-length with instantaneous errors in a style similar to that used in the stochastic gradient [or least mean squares (LMS)] algorithm, the proposed FT algorithm not only retains the advantages from both the SF and the GD algorithms but also has significantly less complexity than existing algorithms. Both performance analysis and numerical simulations are given to verify the new proposed algorithm.

Application of a novel optimal control algorithm to a benchmark fed-batch fermentation process

A novel algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimisation and Parameter Estimation (DISOPE) which has been designed to achieve the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure. A method based on Broyden's ideas is used for approximating some derivative trajectories required. Ways for handling con straints on both manipulated and state variables are described. Further, a method for coping with batch-to- batch dynamic variations in the process, which are common in practice, is introduced. It is shown that the iterative procedure associated with the algorithm naturally suits applications to batch processes. The algorithm is success fully applied to a benchmark problem consisting of the input profile optimisation of a fed-batch fermentation process.

Performance analysis of the unconstrained FxLMS algorithm for active noise control

The paper analyzes the performance of the unconstrained filtered-x LMS (FxLMS) algorithm for active noise control (ANC), where we remove the constraints on the controller that it must be causal and has finite impulse response. It is shown that the unconstrained FxLMS algorithm always converges to, if stable, the true optimum filter, even if the estimation of the secondary path is not perfect, and its final mean square error is independent of the secondary path. Moreover, we show that the sufficient and necessary stability condition for the feedforward unconstrained FxLMS is that the maximum phase error of the secondary path estimation must be within 90°, which is the only necessary condition for the feedback unconstrained FxLMS. The significance of the analysis on a practical system is also discussed. Finally we show how the obtained results can guide us to design a robust feedback ANC headset.

Variable selection for financial distress classification using a genetic algorithm

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

Development and application of a novel algorithm for steady-state optimising control using dynamic information

A novel optimising controller is designed that leads a slow process from a sub-optimal operational condition to the steady-state optimum in a continuous way based on dynamic information. Using standard results from optimisation theory and discrete optimal control, the solution of a steady-state optimisation problem is achieved by solving a receding-horizon optimal control problem which uses derivative and state information from the plant via a shadow model and a state-space identifier. The paper analyzes the steady-state optimality of the procedure, develops algorithms with and without control rate constraints and applies the procedure to a high fidelity simulation study of a distillation column optimisation.

Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.