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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 32 1 Modeling phones, keywords, topics and intents in spoken languages Chen, Wenda. - 2021 BASE Show details 2 A sensorimotor basis of speech communication Bryan, Jacob. - 2019 BASE Show details 3 Polychronization as a mechanism for language acquisition in spiking neural networks Wang, Felix Y. - 2018 BASE Show details 4 Training iCub robot pitch detection with recurrent neural network and LSTM Tang, Steven. - 2018 BASE Show details 5 Unsupervised learning of vocal tract sensory-motor synergies Wagner, William Jacob. - 2017 BASE Show details 6 iCub Tries to Play the Keyboard Chang, Peixin. - 2017 BASE Show details 7 Optimal nonlinear control and estimation using global domain linearization Wendt, Luke Adam. - 2017 Abstract: Alan Turing teaches that cognition is symbol processing. Norbert Wiener teaches that intelligence rests on feedback control. Thus, there are discrete symbols and continuous sensory-motor signals. Sensorimotor dynamics are well-represented by nonlinear differential equations. A possible construction of symbols could be based on equilibria. Language is a symbol system and is one of the highest expressions of cognition. Much of this comes from spatial reasoning, which requires embodied cognition. Spatial reasoning derives from motor function. This thesis introduces a novel generalized non-heuristic method of linearizing nonlinear differential equations over a finite domain. It is used to engineer optimal convergence to target sets, a general form of spatial reasoning. Ordinary differential equations are ubiquitous models in physics and engineering that describe a wide range of phenomena including electromechanical systems. This thesis considers ordinary differential equations expressed in state-space form. For a given initial state, these equations generate signals that are continuous in both time and state. The control engineering objective is to find input functions that steer these states to desired target sets using only the measured output of the system. The state-space domain containing the target set, along with its cost, can be thought of as a symbol for high-level planning. Consider a basis state equal to a vector of nonlinear basis functions, computed from the state, where the state is generated from a multivariate nonlinear dynamic system. The basis state derivative can be expressed as a linear dynamic system with an additional error term. This thesis describes radial basis functions that minimize the error over the entire state-space domain, where the basis state equals zero if and only if the state is in a desired target set. This gives an approximate linear-dynamic system, and if the basis state goes to zero, then the state goes to the target set. This form of linear approximation is global over the domain. Careful selection of the basis gives a fully generalizable relationship between linear stability and nonlinear stability. This form of linearization can be applied to optimal state feedback and state estimation problems. This thesis carefully introduces optimal state feedback control with an emphasis on optimal infinite horizon solutions to linear-dynamic systems that have quadratic cost. A thorough introduction is also given to the optimal output feedback of linear-dynamics systems. The detectable and stabilizable subspaces of a linear-dynamic system are expressed in a generalized closed form. After introducing optimal control for linear systems, this thesis explores adaptive control from several different perspectives including: tuning, system identification, and reinforcement learning. Each of these approaches can be characterized as an optimal nonlinear output feedback problem. In each case, generalized representations can be found using a single layer of appropriately chosen nonlinear basis functions with linear parameterization. The primary focus of this thesis is to select these basis functions, in a fully generalized way, so that they have linear-dynamics. When this can be achieved, infinite horizon state feedback and state estimation can be computed using well-known closed-form solutions. This thesis demonstrates how multivariate nonlinear dynamic systems defined on a finite domain can be approximated by computationally equivalent high-dimensional linear-dynamic systems using a generalized basis state. This basis state is computed with a single layer of biologically inspired radial basis functions. The method of linearization is described as "global domain linearization" because it holds over a specified domain, and therefore provides a global linear approximation with respect to that domain. Any optimal state estimation or state feedback is globally optimal over the domain of linearization. The tools of optimal linear control theory can be applied. In particular, control and estimation problems involving under-actuated under-measured nonlinear systems with generalized nonlinear reward can be solved with closed-form infinite horizon linear-quadratic control and estimation. The controllable, uncontrollable, stabilizable, observable, unobservable, and detectable subspaces can all be described in a meaningful generalized way. State estimation and state feedback can then be implemented in computationally efficient low-dimensional highly nonlinear form. Generalized optimal state estimation and state feedback for continuous-time continuous-state systems is necessary machinery for any high-level symbolic planning that might involve unstable electromechanical systems. Symbols naturally form in the presence of more than one target state. This could provide a natural method of language acquisition. Given a state, all symbolic domains that intersect the state would have equilibrium and cost. These intersections define the legal grammar of symbol transition. An engineer or agent can design these symbols for high-level planning. Generalized infinite horizon state feedback and state estimation can then be computed for the continuous system that each symbol represents using traditional linear tools with domain linearization. Keyword: Approximate; Basis; Continuous; Control; Differential; Domain; Dynamic; Estimation; Feedback; Generalized; Linearization; Nonlinear; Optimal; Radial URL: http://hdl.handle.net/2142/98334 BASE Hide details 8 On the effects of masking of perceptual cues in hearing-impaired ears Cole, Cliston Luther. - 2017 BASE Show details 9 Minimum-error, energy-constrained source coding by sensory neurons Johnson, Erik C.. - 2016 BASE Show details 10 Autoregressive hidden Markov models and the speech signal Bryan, Jacob. - 2014 BASE Show details 11 Robots as language users: a computational model for pragmatic word learning Niehaus, Logan. - 2014 BASE Show details 12 Techniques for understanding hearing-impaired perception of consonant cues Trevino, Andrea. - 2013 BASE Show details 13 Semi-supervised learning for acoustic and prosodic modeling in speech applications Huang, Jui Ting. - 2012 BASE Show details 14 Acoustic model adaptation for recognition of dysarthric speech Sharma, Harsh. - 2012 BASE Show details 15 Computational differences between whispered and non-whispered speech Lim, Boon Pang. - 2011 BASE Show details 16 Autonomous learning of action-word semantics in a humanoid robot Niehaus, Logan. - 2011 BASE Show details 17 Statistical Model Based Multi-Microphone Speech Processing: Toward Overcoming Mismatch Problem Kim, Lae-Hoon. - 2010 BASE Show details 18 Estimation problems in speech and natural language Bhat, Suma P.. - 2010 BASE Show details 19 Extraction of pragmatic and semantic salience from spontaneous spoken English Hasegawa-Johnson, Mark; Levinson, Stephen E.; Zhang, Tong In: Speech communication. - Amsterdam [u.a.] : Elsevier 48 (2006) 3-4, 437-462 BLLDB OLC Linguistik Show details 20 Cognitive state classification in a spoken tutorial dialogue system Zhang, Tong; Hasegawa-Johnson, Mark; Levinson, Stephen E. In: Speech communication. - Amsterdam [u.a.] : Elsevier 48 (2006) 6, 616-632 BLLDB Show details Page: 1 2

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