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.

Reverse microemulsion-mediated synthesis of SiO2-coated ZnO composite nanoparticles : multiple cores with tunable shell thickness

Monodispersed SiO₂-shell/ZnO-core composite nanospheres have been prepared in an oil-in-water microemulsion system. By using cyclohexane as the oil phase and Triton X-100 as the surfactant, composite nanospheres with high core loading levels and tunable shell thickness were obtained. Utilization of PVP capping agent on ZnO allowed the synthesis of composite nanospheres without forming any coreless SiO2 spheres or shell-less ZnO particles. The photoactivity of ZnO nanoparticles was greatly reduced by SiO2-coating, which enables their applications as durable, safe, and nonreactive UV blockers in plastics, coating, and other products.

Tunable wettability of polyimide films based on electrostatic self-assembly of ionic liquids

We have demonstrated that the surface wettability of negatively charged polyimide films could be turned by electrostatic self-assembly of ionic liquids. The water contact angles of the polyimide films varied in the range 27-80 degrees for 13 different ionic liquids based on imidazolium and ammonium salts. The surface morphology of the resulting surfaces was characterized using atomic force microscopy. The results revealed that the assembly of longer-substituent cations was characterized by the formation of spherical nanoparticles that were formed due to sequent aggregation of cations on those electrostatically assembled ones via hydrophobic interaction. In this case, the counteranions are present in the assembled layers and the wettability is accordingly affected. Whereas for shorter-substituent cations, no aggregates were formed due to the less hydrophobic interaction than the electrostatic repulsive interaction between the cations, and the counteranions were absent from the assembled layers. This method can also be utilized to quantify the hydrophobicity of various ionic liquids.

Design and simulation of a tunable MEMS filter for wireless biomedical signal transceivers

This paper presents a new architecture for a high quality tunable MEMS filter that can be used in wireless biomedical signal transceivers. It consists of a π match circuit with two shunt capacitive coupling switches separated by a piece of high impedance short transmission line, and also a series switch placed at the quarter wavelength distance away from the π match circuit. The low actuation voltage and also tunability are important features of the design objective. All portions of the filter can be realized simultaneously. Thus, the filter docs not require any extra steps during its fabrication, and is not costly. The simulation results confirm the good performance of the filter.

Regenerated silk fibroin using protic ionic liquids solvents : towards an all-ionic-liquid process for producing silk with tunable properties

We have shown that protic ionic liquids, pILs, are effective coagulation solvents for the regenerated of silk fibroin, RSF. We show that the choice of pIL has a dramatic effect on the composition of the RSF. Additionally the use of pILs as the coagulator eliminates the need for volatile organic solvents in silk processing.

Boron carbon nitride nanostructures from salt melts : tunable water-soluble phosphors

A simple, high yield, chemical process is developed to fabricate layered h-BN nanosheets and BCNO nanoparticles with a diameter of ca. 5 nm at 700 °C. The use of the eutectic LiCl/KCl salt melt medium enhances the kinetics of the reaction between sodium borohydride and urea or guanidine as well as the dispersion of the nanoparticles in water. The carbon content can be tuned from 0 to 50 mol % by adjusting the reactant ratio, thus providing precise control of the light emission of the particles in the range 440–528 nm while reaching a quantum yield of 26%. Because of their green synthesis, low toxicity, small size, and stability against aggregation in water, the as-obtained photoluminescent BCNO nanoparticles show promise for diagnostics and optoelectronics.