Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify, arbitrarily, neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.Comment: In submission to Frontiers in Neuromorphic Engineerin

Parallel Multistage Wide Neural Network

Deep learning networks have achieved great success in many areas such as in large scale image processing. They usually need large computing resources and time, and process easy and hard samples inefficiently in the same way. Another undesirable problem is that the network generally needs to be retrained to learn new incoming data. Efforts have been made to reduce the computing resources and realize incremental learning by adjusting architectures, such as scalable effort classifiers, multi-grained cascade forest (gc forest), conditional deep learning (CDL), tree CNN, decision tree structure with knowledge transfer (ERDK), forest of decision trees with RBF networks and knowledge transfer (FDRK). In this paper, a parallel multistage wide neural network (PMWNN) is presented. It is composed of multiple stages to classify different parts of data. First, a wide radial basis function (WRBF) network is designed to learn features efficiently in the wide direction. It can work on both vector and image instances, and be trained fast in one epoch using subsampling and least squares (LS). Secondly, successive stages of WRBF networks are combined to make up the PMWNN. Each stage focuses on the misclassified samples of the previous stage. It can stop growing at an early stage, and a stage can be added incrementally when new training data is acquired. Finally, the stages of the PMWNN can be tested in parallel, thus speeding up the testing process. To sum up, the proposed PMWNN network has the advantages of (1) fast training, (2) optimized computing resources, (3) incremental learning, and (4) parallel testing with stages. The experimental results with the MNIST, a number of large hyperspectral remote sensing data, CVL single digits, SVHN datasets, and audio signal datasets show that the WRBF and PMWNN have the competitive accuracy compared to learning models such as stacked auto encoders, deep belief nets, SVM, MLP, LeNet-5, RBF network, recently proposed CDL, broad learning, gc forest etc. In fact, the PMWNN has often the best classification performance

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Structured Semidefinite Programming for Recovering Structured Preconditioners

We develop a general framework for finding approximately-optimal preconditioners for solving linear systems. Leveraging this framework we obtain improved runtimes for fundamental preconditioning and linear system solving problems including the following. We give an algorithm which, given positive definite $\mathbf{K} \in \mathbb{R}^{d \times d}$ with $\mathrm{nnz}(\mathbf{K})$ nonzero entries, computes an $\epsilon$-optimal diagonal preconditioner in time $\widetilde{O}(\mathrm{nnz}(\mathbf{K}) \cdot \mathrm{poly}(\kappa^\star,\epsilon^{-1}))$, where $\kappa^\star$ is the optimal condition number of the rescaled matrix. We give an algorithm which, given $\mathbf{M} \in \mathbb{R}^{d \times d}$ that is either the pseudoinverse of a graph Laplacian matrix or a constant spectral approximation of one, solves linear systems in $\mathbf{M}$ in $\widetilde{O}(d^2)$ time. Our diagonal preconditioning results improve state-of-the-art runtimes of $\Omega(d^{3.5})$ attained by general-purpose semidefinite programming, and our solvers improve state-of-the-art runtimes of $\Omega(d^{\omega})$ where $\omega > 2.3$ is the current matrix multiplication constant. We attain our results via new algorithms for a class of semidefinite programs (SDPs) we call matrix-dictionary approximation SDPs, which we leverage to solve an associated problem we call matrix-dictionary recovery.Comment: Merge of arXiv:1812.06295 and arXiv:2008.0172

Nonlinear Control Strategies for Outdoor Aerial Manipulators

In this thesis, the design, validation and implementation of nonlinear control strategies for aerial manipulators {i.e. aerial robots equipped with manipulators{ is studied, with special emphasis on the internal coupling of the system and its resilience against external disturbances. For the rst, di erent decentralised control strategies {i.e. using di erent control typologies for each one of the subsystems{ that indirectly take into account this coupling have been analysed. As a result, a nonlinear strategy composed of two controllers is proposed. A higher priority is given to the manipulation accuracy, relaxing the platform tracking, and hence obtaining a solution improving the manipulation capabilities with the surrounding environment. To validate these results, thorough stability and robustness analyses are provided, both theoretically and in simulation. On the other hand, a signi cant e ort has been devoted to improving the response and applicability of robot manipulators used in ight via control. In particular, the design of controllers for lightweight exible manipulators {that reduce the consequences of incidents involving unforeseen contacts{ is analysed. Although their inherent nature perfectly ts for aerial manipulation applications, the added exibility produces unwanted behaviours, such as second-order modes and uncertainties. To cope with them, an adaptable position nonlinear control strategy is proposed. To validate this contribution, the stability of the approach is studied in theory and its capabilities are proven in several experimental scenarios. In these, the robustness of the solution against unforeseen impacts and contact with uncharacterised interfaces is demonstrated. Subsequently, this strategy has been enriched with {multiaxis{ force control capabilities thanks to the inclusion of an outer control loop modifying the manipulator reference. Accordingly, this additional applicationfocused capability is added to the controlled system without loosing the modulated response of the inner-loop position strategy. It is also worth noting that, thanks to the cascade-like nature of the modi cation, the transition between position and force control modes is inherently smooth and automatic. The stability of this expanded strategy has been theoretically analysed and the results validated in a set of experimental scenarios. To validate the rst nonlinear approach with realistic outdoor simulations before its implementation, a computational uid dynamics analysis has been performed to obtain an explicit model of the aerodynamic forces and torques applied to the blunt-body of the aerial platform in ight. The results of this study have been compared to the most common alternative nowadays, being highlighted that the proposed model signi cantly surpasses this option in terms of accuracy. Moreover, it is worth underscoring that this characterisation could be also employed in the future to develop control solutions with enhanced rejection capabilities against wind conditions. Finally, as the focus of this thesis is on the use of novel control strategies on real aerial manipulation outdoors to improve their accuracy while performing complex tasks, a modular autopilot solution to be able to implement them has been also developed. This general-purpose autopilot allows the implementation of new algorithms, and facilitates their theory-to-experimentation transition. Taking into account this perspective, the proposed tool employs the simple and widely-known MAS interface and the highly reliable PX4 autopilot as backup, thus providing a redundant approach to handle unexpected incidents in ight.En esta tesis se ha estudiado el diseño, validación e implementación de estrategias de control no lineales para robots manipuladores aéreos –esto es, robots aéreos equipados con un sistema de manipulación robótica–, dándose especial énfasis a las interacciones internas del sistema y a su resiliencia frente a efectos externos. Para lo primero, se han analizado diferentes estrategias de control descentralizado –es decir, que usan tipologías de control diferentes para cada uno de los subsistemas–, pero que tienen indirectamente en consideración la interacción entre manipulación y vuelo. Como resultado de esta línea, se propone una estretegia de control conformada por dos controladores. Estos se coordinan de tal forma que se le da prioridad a la manipulación sobre el seguimiento de posiciones del vehículo, produciéndose un sistema de control que mejora la precisión de las interacciones entre el sistema manipulador y el entorno. Para validar estos resultados, se ha analizado su estabilidad y robustez tanto teóricamente como mediante simulaciones numéricas. Por otro lado, se ha buscado mejorar la respuesta y aplicabilidad de los manipuladores que se usan en vuelo mediante su control. Dentro de esta tendencia, la tesis se ha centrado en el diseño de controladores para manipuladores ligeros flexibles, ya que estos permiten reducir el peso del sistema completo y reducen el riesgo de incidentes debidos a contactos inesperados. Sin embargo, la flexibilidad de estos produce comportamientos indeseados durante la operación, como la aparición de modos de segundo orden y cierta incentidumbre en su comportamiento. Para reducir su impacto en la precisión de las tareas de manipulación, se ha desarrollado un controlador no lineal adaptable. Para validar estos resultados, se ha analizado la estabilidad del sistema teóricamente y se han desarrollado una serie de experimentos. En ellos, se ha comprobado su robustez ante impactos inesperados y contactos con elementos no caracterizados. Posteriormente, esta estrategia para manipuladores flexibles ha sido ampliada al añadir un bucle externo que posibilita el control en fuerzas en varias direcciones. Esto permite, mediante un único controlador, mantener la suave respuesta de la estrategia. Además cabe destacar que, al contar esta estrategia con un diseño en cascade, la transición entre los segmentos de desplazamiento del brazo y de aplicación de fuerzas es fluida y automática. La estabilidad de esta estrategia ampliada ha sido analizada teóricamente y los resultados han sido validados experimentalmente. Para validar la primera estrategia mediante simulaciones que representen fielmente las condiciones en exteriores antes de su implementación, ha sido necesario realizar un estudio mediante mecánica de fluidos computacional para obtener un modelo explícito de las fuerzas y momentos aerodinámicos a los que se efrenta la plataforma en vuelo. Los resultados de este estudio han sido comparados con la alternativa más empleada actualmente, mostrándose que los avances del método propuesto son sustanciales. Asimismo, es importante destacar que esta caracterización podría también usarse en el futuro para desarrollar controladores con una respuesta mejorada ante perturbaciones aerodinámicas, como en el caso de volar con viento. Finalmente, al ser esta una tesis centrada en las estrategias de control novedosas en sistemas reales para la mejora de su rendimiento en misiones complejas, se ha desarrollado un autopiloto modular fácilmente modificable para implementarlas. Este permite validar experimentalmente nuevos algoritmos y facilita la transición entre teoría y práctica. Para ello, esta herramienta se basa en una interfaz sencilla ampliamente conocida por los investigadores de robótica, Simulink®, y cuenta con un autopiloto de respaldo, PX4, para enfrentarse a los incidentes inesperados que pudieran surgir en vuelo

Training issues and learning algorithms for feedforward and recurrent neural networks

Ph.DDOCTOR OF PHILOSOPH

A neural network-based exploratory learning and motor planning system for co-robots

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object