A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Orthogonal forward selection for constructing the radial basis function network with tunable nodes

An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

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.

pH-tunable hydrogelators for water purification: structural optimisation and evaluation

A focused library of potential hydrogelators each containing two substituted aromatic residues separated by a urea or thiourea linkage have been synthesised and characterized. Six of these novel compounds are highly efficient hydrogelators, forming gels in aqueous solution at low concentrations (0.03–0.60 wt %). Gels were formed through a pH switching methodology, by acidification of a basic solution (pH 14 to ≈4) either by addition of HCl or via the slow hydrolysis of glucono-δ-lactone. Frequently, gelation was accompanied by a dramatic switch in the absorption spectra of the gelators, resulting in a significant change in colour, typically from a vibrant orange to pale yellow. Each of the gels was capable of sequestering significant quantities of the aromatic cationic dye, methylene blue, from aqueous solution (up to 1.02 g of dye per gram of dry gelator). Cryo-transmission electron microscopy of two of the gels revealed an extensive network of high aspect ratio fibers. The structure of the fibers altered dramatically upon addition of 20 wt % of the dye, resulting in aggregation and significant shortening of the fibrils. This study demonstrates the feasibility for these novel gels finding application as inexpensive and effective water purification platforms.

Adaptive noise cancellation with fast tunable RBF network

This paper describes a novel adaptive noise cancellation system with fast tunable radial basis function (RBF). The weight coefficients of the RBF network are adapted by the multi-innovation recursive least square (MRLS) algorithm. If the RBF network performs poorly despite of the weight adaptation, an insignificant node with little contribution to the overall performance is replaced with a new node without changing the model size. Otherwise, the RBF network structure remains unchanged and only the weight vector is adapted. The simulation results show that the proposed approach can well cancel the noise in both stationary and nonstationary ANC systems.

Online modeling with tunable RBF network

In this paper, we propose a novel online modeling algorithm for nonlinear and nonstationary systems using a radial basis function (RBF) neural network with a fixed number of hidden nodes. Each of the RBF basis functions has a tunable center vector and an adjustable diagonal covariance matrix. A multi-innovation recursive least square (MRLS) algorithm is applied to update the weights of RBF online, while the modeling performance is monitored. When the modeling residual of the RBF network becomes large in spite of the weight adaptation, a node identified as insignificant is replaced with a new node, for which the tunable center vector and diagonal covariance matrix are optimized using the quantum particle swarm optimization (QPSO) algorithm. The major contribution is to combine the MRLS weight adaptation and QPSO node structure optimization in an innovative way so that it can track well the local characteristic in the nonstationary system with a very sparse model. Simulation results show that the proposed algorithm has significantly better performance than existing approaches.

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

Sparse density estimator with tunable kernels

A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

(85)Rb Bose-Einstein condensate with tunable interaction: A quantum many body approach

We present a quantum many body approach with van der Waal type of interaction to achieve (85)Rb Bose-Einstein condensate with tunable interaction which has been produced by magnetic field induced Feshbach resonance in the JILA experiment. (C) 2008 Elsevier B.V. All rights reserved.

Tunable visible photoluminescence of powdered silica glass

intense photoluminescence in the visible region was observed at room temperature in standard soda-lime-silica glass powder, mechanically milled in a high-energy attrition mill. The emission band maximum shows an interesting dependence on the exciting wavelength, suggesting the possibility to tune the PL emission. These findings indicate that the photoluminescence may be directly related to unsatisfied chemical bonds correlated with the high surface area. The Raman scattering and ultraviolet-visible optical reflectance measurements corroborate this assertion. Transmission electron microscopy measurements indicate that samples milled more than 10 h present the formation of nanocrystallites with about 10-20 nm. (C) 2007 Elsevier B.V. All rights reserved.

A tunable triode-MOSFET transconductor and its application to g(m)-C filters

A linear, tunable CMOS transconductance stage is introduced. Drain voltage of the input transistor operating in triode region is settled by a regulation loop and a first-order linear relationship between g(m) and a de bias voltage is achieved. In addition to easy tuning, this technique offers circuit simplicity, wide dynamic range, high input and output impedances and low consumption. The transconductor is presented on both single-ended and fully-differential versions. A 3rd-order elliptical low-pass g(m)-C filter with a nominal roll-off frequency of 2MHz is used as one example for the many applications of the proposed transconductor. SPICE data describe circuits performances and filter tunabilily Passband is tuned at a rate of 2.36KHz/mV and good linearity is indicated by a 0.89% THD for an 800mV(p-p) balanced-driven input.

On designing linear tunable high-Q OTA-C filters with low sensitivity

This paper discusses a design approach for a high-Q low-sensitivity OTA-C biquad bandpass section. An optimal relationship is established between transconductances defining the differencebeta - gamma in the Q-factor denominator, setting the Q-sensitivity to tuning voltages around unity. A 30-MHz filter was designed based on a 0.35 mum CMOS process and V-DD=3.3 V. A range of circuit simulation supports the theoretical analysis. Q-factor spans from 20.5 to 60, while ensuring filter stability along the tuning range. Although a triode-operating OTA is used, the procedure can be extended to other types of transconductor.

A linearly tunable low-voltage CMOS transconductor with improved common-mode stability and its application to (gm)-C filters

A linearly tunable low-voltage CMOS transconductor featuring a new adaptative-bias mechanism that considerably improves the stability of the processed-signal common,mode voltage over the tuning range, critical for very-low voltage applications, is introduced. It embeds a feedback loop that holds input devices on triode region while boosting the output resistance. Analysis of the integrator frequency response gives an insight into the location of secondary poles and zeros as function of design parameters. A third-order low-pass Cauer filter employing the proposed transconductor was designed and integrated on a 0.8-mum n-well CMOS standard process. For a 1.8-V supply, filter characterization revealed f(p) = 0.93 MHz, f(s) = 1.82 MHz, A(min) = 44.08, dB, and A(max) = 0.64 dB at nominal tuning. Mined by a de voltage V-TUNE, the filter bandwidth was linearly adjusted at a rate of 11.48 kHz/mV over nearly one frequency decade. A maximum 13-mV deviation on the common-mode voltage at the filter output was measured over the interval 25 mV less than or equal to V-TUNE less than or equal to 200 mV. For V-out = 300 mV(pp) and V-TUNE = 100 mV, THD was -55.4 dB. Noise spectral density was 0.84 muV/Hz(1/2) @1 kHz and S/N = 41 dB @ V-out = 300 mV(pp) and 1-MHz bandwidth. Idle power consumption was 1.73 mW @V-TUNE = 100 mV. A tradeoff between dynamic range, bandwidth, power consumption, and chip area has then been achieved.