The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Chia-feng Juang - One of the best experts on this subject based on the ideXlab platform.
-
A Recurrent Fuzzy-Network-Based Inverse Modeling Method for a Temperature System Control
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2007Co-Authors: Chia-feng Juang, Jung-shing ChenAbstract:Temperature control by a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN) designed by modeling plant inverse is proposed in this paper. TRFN is a recurrent fuzzy network developed from a series of TSK-type fuzzy if--then rules, and is characterized by structure and parameter learning. In parameter learning, two types of learning algorithms, the Kalman filter and the gradient descent learning algorithms, are applied to consequent parameters depending on the learning situation. The TRFN has the following advantages when applied to temperature control problems: 1) high learning ability, which considerably reduces the controller training time; 2) no a priori knowledge of the plant order is required, which eases the design process; 3) good and robust control performance; 4) online learning ability, i.e., the TRFN can adapt itself to unpredictable plant changes. The TRFN-based direct inverse control configuration is applied to a real water bath temperature control plant, where various control conditions are experimented. The same experiments are also performed by proportional-integral (PI), fuzzy, and neural network controllers. From comparisons, the aforementioned advantages of a TRFN have been verified
-
Mold temperature control of a rubber injection-molding machine by TSK-type recurrent neural fuzzy network
Neurocomputing, 2006Co-Authors: Chia-feng Juang, Shui-tien HuangAbstract:Abstract Practical mold temperature control of a rubber injection-molding machine is studied in this paper. The controller used is a recurrent fuzzy network called Takagi–Sugeno–Kang (TSK)-type recurrent fuzzy network (TRFN), which is characterized by its recurrent structure, on-line structure and parameter learning. Due to the powerful learning ability of TRFN, a simple controller design scheme using direct inverse configuration is proposed. With recurrent structure in TRFN, no a priori knowledge of the molding machine order is required. The designed TRFN controller performs well even if the sampling interval is different from the original one used for training. The design of TRFN consists of off-line and on-line training. For off-line learning, structure and parameter of TRFN are learned, and the consequent part parameters are tuned by Kalman filter algorithm. On-line learning is performed to fine tune the consequent parameters of TRFN and achieve a better control performance with the use a simple gradient descent algorithm. Practical experiments and comparisons with other types of controllers demonstrate the performance of the proposed TRFN controller.
-
a tsk type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms
IEEE Transactions on Fuzzy Systems, 2002Co-Authors: Chia-feng JuangAbstract:In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal calls for the design of TRFN by either neural network or genetic algorithms depending on the learning environment. A recurrent fuzzy network is described which develops from a series of recurrent fuzzy if-then rules with TSK-type consequent parts. The recurrent property comes from feeding the internal variables, derived from fuzzy firing strengths, back to both the network input and output layers. In this configuration, each internal variable is responsible for memorizing the temporal history of its corresponding fuzzy rule. The internal variable is also combined with external input variables in each rule's consequence, which shows an increase in network learning ability. TRFN design under different learning environments is next advanced. For problems where supervised training data is directly available, TRFN with supervised learning (TRFN-S) is proposed, and a neural network (NN) learning approach is adopted for TRFN-S design. An online learning algorithm with concurrent structure and parameter learning is proposed. With flexibility of partition in the precondition part, and outcome of TSK-type, the TRFN-S displays both small network size and high learning accuracy. For problems where gradient information for NN learning is costly to obtain or unavailable, like reinforcement learning, TRFN with Genetic learning (TRFN-G) is put forward. The precondition parts of TRFN-G are also partitioned in a flexible way, and all free parameters are designed concurrently by genetic algorithm. Owing to the well-designed network structure of TRFN, TRFN-G, like TRFN-S, is characterized by high learning accuracy. To demonstrate the superior properties of TRFN, TRFN-S is applied to dynamic system identification and TRFN-G to dynamic system control. By comparing the results to other types of recurrent networks and design configurations, the efficiency of TRFN is verified.
Joachim Ballmann - One of the best experts on this subject based on the ideXlab platform.
-
phospha derivatives of tris 2 aminoethyl amine tren and tris 3 aminopropyl amine TRPN synthesis and complexation studies with group 4 metals
Organometallics, 2015Co-Authors: Malte Sietzen, Sonja Batke, Hubert Wadepohl, Lukas S Merz, Joachim BallmannAbstract:The N,N′,N″-triphenyl-substituted derivative of tris(2-aminoethyl)phosphine (Ph3-phospha-tren, P(CH2CH2NHR)3, R = Ph) and four derivatives of the related tris(3-aminopropyl)phosphine (phospha-TRPN, P(CH2CH2CH2NHR)3, R = iPr, tBu, SitBuMe2, Ph) have been synthesized in addition to the parent phospha-TRPN. Out of these ligand systems, only the N,N′,N″-triphenyl-substituted phospha-TRPN derivative P(CH2CH2CH2NHPh)3 was found to be suitable for coordination to group 4 metals. For titanium, zirconium, and hafnium, the C3-symmetric endo-P-configured dimethylamido complexes Ph[PN3]M(NMe2) of the former ligand have been prepared and converted into the corresponding triflates Ph[PN3]M(OTf). Starting from these triflates, the benzyl complexes Ph[PN3]M(Bn) (M = Ti, Zr, Hf) have been obtained via reaction with Bn2Mg(THF)2. In case of titanium, the benzyl species Ph[PN3]Ti(Bn) is prone to thermal elimination of toluene, which results in the formation of a cyclometalated species. These findings are discussed in context...
-
Phospha Derivatives of Tris(2-aminoethyl)amine (tren) and Tris(3-aminopropyl)amine (TRPN): Synthesis and Complexation Studies with Group 4 Metals
2015Co-Authors: Malte Sietzen, Sonja Batke, Lukas Merz, Hubert Wadepohl, Joachim BallmannAbstract:The N,N′,N″-triphenyl-substituted derivative of tris(2-aminoethyl)phosphine (Ph3-phospha-tren, P(CH2CH2NHR)3, R = Ph) and four derivatives of the related tris(3-aminopropyl)phosphine (phospha-TRPN, P(CH2CH2CH2NHR)3, R = iPr, tBu, SitBuMe2, Ph) have been synthesized in addition to the parent phospha-TRPN. Out of these ligand systems, only the N,N′,N″-triphenyl-substituted phospha-TRPN derivative P(CH2CH2CH2NHPh)3 was found to be suitable for coordination to group 4 metals. For titanium, zirconium, and hafnium, the C3-symmetric endo-P-configured dimethylamido complexes Ph[PN3]M(NMe2) of the former ligand have been prepared and converted into the corresponding triflates Ph[PN3]M(OTf). Starting from these triflates, the benzyl complexes Ph[PN3]M(Bn) (M = Ti, Zr, Hf) have been obtained via reaction with Bn2Mg(THF)2. In case of titanium, the benzyl species Ph[PN3]Ti(Bn) is prone to thermal elimination of toluene, which results in the formation of a cyclometalated species. These findings are discussed in context with the very few group 4 trisamidophosphine complexes that have been reported earlier
Md Abdus Salam - One of the best experts on this subject based on the ideXlab platform.
-
metal ion interactions with nucleobases in the tripodal tris 2 aminoethyl amine tren ligand system crystal structures of zn tren adeninato clo4 cd tren adeninato clo4 ni tren adeninato clo4 zn tren hypoxanthinato clo4 h2o cd tren 2 hypoxanthinato clo
Inorganica Chimica Acta, 2009Co-Authors: Md Abdus Salam, Hou Qun Yuan, Takanori Kikuchi, Nilesh Anand Prasad, Ikuhide Fujisawa, Katsuyuki AokiAbstract:Abstract Reaction between nucleobases (adenine, hypoxanthine, cytosine, and uracil), tris(2-aminoethyl)amine (tren) and M(ClO4)2 (M2+ = Zn2+, Cd2+, Ni2+ or Cu2+) under pH 8–9 gave eight ternary tren-metal ion-nucleobase complexes, among which seven crystal structures, [Zn(tren)(adeninato)] · ClO4 (1), [Cd(tren)(adeninato)] · ClO4 (2), [Ni(tren)(adeninato)(ClO4)] (3), [Zn(tren)(hypoxanthinato)] · ClO4 · H2O (4), [{Cd(tren)}2(hypoxanthinato)] · (ClO4)3 · 0.5 H2O (5), [Ni(tren)(uracilato)(H2O)] · ClO4 · (0.5H2O)2 (7), and [Cu(tren)(uracilato)] · ClO4 · 0.5H2O (8) were determined by X-ray diffraction. In each of the adenine complexes with Zn2+ (1), Cd2+ (2), and Ni2+ (3), among which 1 and 2 are isostructural to each other, a tren-capped metal ion binds to an adeninato ligand through N(9) with the formation of an intramolecular interligand hydrogen bond between the amino nitrogen of tren and N(3) of the base. In the hypoxanthine complexes 4 and 5, a tren-capped Zn2+ ion in 4 binds to a hypoxanthinato ligand through N(9) with the formation of an intramolecular N(tren)–H⋯N(3) hydrogen bond, while in 5 two tren-capped Cd2+ ions bind to a hypoxanthinato ligand, one through N(7) with the formation of an intramolecular N(tren)–H⋯O(6) hydrogen bond and the other through N(9) to form an intramolecular N(tren)–H⋯N(3) hydrogen bond. In each of the uracil complexes with Ni2+ (7) and Cu2+ (8), a tren-capped metal ion binds to an uracilato ligand through N(1) with the formation of an intracomplex O(aqua)–H⋯O(2) hydrogen bond in 7 or an intramolecular N(tren)–H⋯O(2) hydrogen bond in 8. Tren-capped metal ion-bonded nucleobases act as building blocks for the assembly of supramolecular structures, by versatile hydrogen bonding and/or stacking abilities of nucleobases. The significance of intramolecular interligand interaction as a factor that affects metal-binding site(s) on nucleobases is emphasized.
Nadia M Shuaib - One of the best experts on this subject based on the ideXlab platform.
-
synthesis and characterization of binuclear and polymeric five coordinate copper ii complexes derived from 3 3 3 triaminotripropylamine TRPN
Polyhedron, 1999Co-Authors: Salah S Massoud, Franz A Mautner, Morsy A M Abuyoussef, Nadia M ShuaibAbstract:Abstract The synthesis and characterization of five-coordinate copper(II) complexes derived from 3,3′,3″-triaminotripropylamine (TRPN) are described. The X-ray diffraction studies have established the structures [Cu(TRPN)(N3)]ClO4 (I) and [Cu2(TRPN)(tren)(NO2)(H2O)](ClO4)3 (II). Compound (I) consists of a polymeric cation chain and ClO4− counter ions. The coordination geometry of the Cu(II) centers may be described as distorted square pyramidal (SP) with the azido group at the apical site and three nitrogen donors of the TRPN molecule occupy the basal sites. The CuN5 chromophore is completed by a bridging aminopropyl group of neighboring TRPN ligand. Compound (II) is a dinuclear complex cation with two different cation geometries in the unit cell. The geometry of the Cu(II) binding tren ligand is close to trigonal bipyramidal (TBP), with the basal and apical sites are occupied by the four nitrogen atoms of the tren ligand. The fifth coordination site is bridged to one of the aminopropyl arms of the TRPN ligand. The geometry of the second Cu(II) center may be described as close to distorted SP, where the TRPN ligand is binding the Cu(II) ion via the two primary aminopropyl groups and the tertiary nitrogen. The remaining two sites are occupied by oxygen atoms of a water molecule and nitrite ion. The intramolecular Cu…Cu distance in I and II ranges from 7.55 A to 7.94 A. The visible spectra of the complexes in DMSO are consistent with the X-ray results found for I and II and show a greater tendency toward SP geometry.
Thomas Corbitt - One of the best experts on this subject based on the ideXlab platform.
-
Measurement of quantum back action in the audio band at room temperature
Nature, 2019Co-Authors: Jonathan Cripe, David Follman, Nancy Aggarwal, Adam Libson, Robinjeet Singh, Garrett D. Cole, Robert Lanza, Nergis Mavalvala, Thomas CorbittAbstract:Quantum mechanics places a fundamental limit on the precision of continuous measurements. The Heisenberg uncertainty principle dictates that as the precision of a measurement of an observable (for example, position) increases, back action creates increased uncertainty in the conjugate variable (for example, momentum). In interferometric gravitational-wave detectors, higher laser powers reduce the position uncertainty created by shot noise (the photon-counting error caused by the quantum nature of the laser) but necessarily do so at the expense of back action in the form of quantum radiation pressure noise (QRPN)^ 1 . Once at design sensitivity, the gravitational-wave detectors Advanced LIGO^ 2 , VIRGO^ 3 and KAGRA^ 4 will be limited by QRPN at frequencies between 10 hertz and 100 hertz. There exist several proposals to improve the sensitivity of gravitational-wave detectors by mitigating QRPN^ 5 – 10 , but until now no platform has allowed for experimental tests of these ideas. Here we present a broadband measurement of QRPN at room temperature at frequencies relevant to gravitational-wave detectors. The noise spectrum obtained shows effects due to QRPN between about 2 kilohertz and 100 kilohertz, and the measured magnitude of QRPN agrees with our model. We now have a testbed for studying techniques with which to mitigate quantum back action, such as variational readout and squeezed light injection^ 7 , with the aim of improving the sensitivity of future gravitational-wave detectors. Future gravitational-wave detectors are expected to be limited by quantum back action, which is now found in the audio band in a low-loss optomechanical system.
-
measurement of quantum back action in the audio band at room temperature
Nature, 2019Co-Authors: Jonathan Cripe, David Follman, Nancy Aggarwal, Adam Libson, Robinjeet Singh, Garrett D. Cole, Robert Lanza, Nergis Mavalvala, Thomas CorbittAbstract:Quantum mechanics places a fundamental limit on the precision of continuous measurements. The Heisenberg uncertainty principle dictates that as the precision of a measurement of an observable (for example, position) increases, back action creates increased uncertainty in the conjugate variable (for example, momentum). In interferometric gravitational-wave detectors, higher laser powers reduce the position uncertainty created by shot noise (the photon-counting error caused by the quantum nature of the laser) but necessarily do so at the expense of back action in the form of quantum radiation pressure noise (QRPN)1. Once at design sensitivity, the gravitational-wave detectors Advanced LIGO2, VIRGO3 and KAGRA4 will be limited by QRPN at frequencies between 10 hertz and 100 hertz. There exist several proposals to improve the sensitivity of gravitational-wave detectors by mitigating QRPN5–10, but until now no platform has allowed for experimental tests of these ideas. Here we present a broadband measurement of QRPN at room temperature at frequencies relevant to gravitational-wave detectors. The noise spectrum obtained shows effects due to QRPN between about 2 kilohertz and 100 kilohertz, and the measured magnitude of QRPN agrees with our model. We now have a testbed for studying techniques with which to mitigate quantum back action, such as variational readout and squeezed light injection7, with the aim of improving the sensitivity of future gravitational-wave detectors.
