Computational chaos in massively parallel neural networks

A fundamental issue which directly impacts the scalability of current theoretical neural network models to massively parallel embodiments, in both software as well as hardware, is the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the possible emergence of chaotic manifestations. Previous analyses attributed dynamical instability to the topology of the interconnection matrix, to parasitic components or to propagation delays. However, researchers have observed the existence of emergent computational chaos in a concurrently asynchronous framework, independent of the network topology. Researcher present a methodology enabling the effective asynchronous operation of large-scale neural networks. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are computed formally to characterize the underlying nonlinear dynamics. Simulation results are presented to illustrate network convergence to the correct results, even in the presence of large delays

Computational neural learning formalisms for manipulator inverse kinematics

An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints

Wavelet-Based Real-Time Diagnosis of Complex Systems

A new method of robust, autonomous real-time diagnosis of a time-varying complex system (e.g., a spacecraft, an advanced aircraft, or a process-control system) is presented here. It is based upon the characterization and comparison of (1) the execution of software, as reported by discrete data, and (2) data from sensors that monitor the physical state of the system, such as performance sensors or similar quantitative time-varying measurements. By taking account of the relationship between execution of, and the responses to, software commands, this method satisfies a key requirement for robust autonomous diagnosis, namely, ensuring that control is maintained and followed. Such monitoring of control software requires that estimates of the state of the system, as represented within the control software itself, are representative of the physical behavior of the system. In this method, data from sensors and discrete command data are analyzed simultaneously and compared to determine their correlation. If the sensed physical state of the system differs from the software estimate (see figure) or if the system fails to perform a transition as commanded by software, or such a transition occurs without the associated command, the system has experienced a control fault. This method provides a means of detecting such divergent behavior and automatically generating an appropriate warning

Sliding mode control method having terminal convergence in finite time

An object of this invention is to provide robust nonlinear controllers for robotic operations in unstructured environments based upon a new class of closed loop sliding control methods, sometimes denoted terminal sliders, where the new class will enforce closed-loop control convergence to equilibrium in finite time. Improved performance results from the elimination of high frequency control switching previously employed for robustness to parametric uncertainties. Improved performance also results from the dependence of terminal slider stability upon the rate of change of uncertainties over the sliding surface rather than the magnitude of the uncertainty itself for robust control. Terminal sliding mode control also yields improved convergence where convergence time is finite and is to be controlled. A further object is to apply terminal sliders to robot manipulator control and benchmark performance with the traditional computed torque control method and provide for design of control parameters

Computational Neural Learning Formalisms for Perceptual Manipulation: Singularity Interaction Dynamics Model.

This dissertation addresses a fundamental problem in computational AI--developing a class of massively parallel, neural algorithms for learning robustly, and in real-time, complex nonlinear transformations from representative exemplars. Provision of such a capability is at the core of many real-life problems in robotics, signal processing and control. The concepts of terminal attractors in dynamical systems theory and adjoint operators in nonlinear sensitivity theory are exploited to provide a firm mathematical foundation for learning such mappings with dynamical neural networks, while achieving a dramatic reduction in the overall computational costs. Further, we derive an efficient methodology for handling a multiplicity of application-specific constraints during run-time, that precludes additional retraining or disturbing the synaptic structure of the learned network. The scalability of proposed theoretical models to large-scale embodiments in neural hardware is analyzed. Neurodynamical parameters, e.g., decay constants, response gains, etc., are systematically analyzed to understand their implications on network scalability, convergence, throughput and fault tolerance, during both concurrent simulations and implementation in concurrently asynchronous VLSI, optical and opto-electronic hardware. Dynamical diagnostics, e.g., Lyapunov exponents, are used to formally characterize the widely observed dynamical instability in neural networks as emergent computational chaos . Using contracting operators and nonconstructive theorems from fixed point theory, we rigorously derive necessary and sufficient conditions for eliminating all oscillatory and chaotic behavior in additive-type networks. Extensive benchmarking experiments are conducted with arbitrarily large neural networks (over 100 million interconnects) to verify the methodological robustness of our network conditioning formalisms. Finally, we provide insight for exploiting our proposed repertoire of neural learning formalisms in addressing a fundamental problem in robotics--manipulation controller design for robots operating in unpredictable environments. Using some recent results in task analysis and dynamic modeling we develop the Perceptual Manipulation Architecture . The architecture, conceptualized within a perceptual framework, is shown to be well beyond the state-of-the-art model-directed robotics. For a stronger physical interpretation of its implications, our discussions are embedded in context of a novel systems\u27 concept for automated space operations

Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems

Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic

Comparative evaluation of equine mesenchymal stem cells derived from amniotic fluid and umbilical cord blood

Mesenchymal stem cells (MSCs) are promising therapeutic tools for the treatment of tendon rupture and other musculoskeletal injuries in horses. Although MSCs from bone marrow and adipose tissues are commonly used for therapeutic purpose in equines, umbilical cord blood (UCB) and amniotic fluid (AF) are potential non-invasive sources of MSCs. We collected AF and UCB from twenty mares during foaling for isolation of MSCs and evaluated them for the differences in isolation rates, proliferation capacity, expression of MSC markers and multi-lineage differentiation ability. The plastic adherent colonies were observed in 60% AF and 65% UCB samples. The mean doubling time for AF cells was significantly lower than that of UCB cells. The AF-MSCs proliferated till passage 36 whereas UCB-MSCs till passage 20 only. Both AF and UCB derived cells expressed CD29, CD44, CD73, CD90 and CD105 and were negative for haematopoietic and leukocytic markers (CD14, CD34 and CD45). The CD90 and CD73 expression was significantly higher in AF derived cells as compared to UCB-MSCs. On the other hand, CD29 expression was significantly lower in AF derived cells as compared to UCB derived cells. The UCB-MSCs differentiated poorly to adipogenic lineage compared to AF-MSCs. These results suggested that equine AF yields more MSCs with greater in vitro proliferation and differentiation capacities and is better non-invasive source of MSCs for regenerative therapies in equines

Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science

Diabetes is a rapidly spreading disease. It occurs when the pancreas produces insufficient insulin or the body cannot utilise it effectively. Diabetic retinopathy (DR) and blindness are two major issues for diabetics. Diabetes patients increase the amount of data collected about DR. To extract important information and undiscovered knowledge from data, data mining techniques are required. DM is necessary in DR to improve society's health. The study focuses on the early detection of diabetic retinopathy using patient information. DM approaches are used to extract information from these numeric records. The dataset was used to forecast DR using logistic regression, KNN, SVM, bagged tree, and boosted tree classifiers. Two cross-validations are used to find the best features and avoid overfitting. The dataset includes 900 diabetes patients. The boosted tree produced the best classification accuracy (90.1%) with 10% hold-out validation. KNN also achieved 88.9% accuracy, which is impressive. As a result, the research suggests that bagged trees and KNN are good classifiers for DR