James T. Lo

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A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons(More)
Multilayer perceptrons (MLPs) with long- and short-term memories (LASTMs) are proposed for adaptive processing. The activation functions of the output neurons of such a network are linear, and thus the weights in the last layer affect the outputs of the network linearly and are called linear weights. These linear weights constitute the short-term memory and(More)
A function : R + ! R + belongs to class-KR iff it belongs to class-K and limr!1 (r) = 1. Repetitive control system: A new type servo system for periodic exogenous signals, " IEEE Trans. A discrete-time learning control algorithm for a class of linear time-invariant, " IEEE Trans. Study on the robustness of PID type iterative learning control with current(More)
By the fundamental neural filtering theorem, a properly trained recursive neural filter with fixed weights that processes only the measurement process generates recursively the conditional expectation of the signal process with respect to the joint probability distributions of the signal and measurement processes and any uncertain environmental process(More)
  • James Lo
  • 1977
the unit sphere, the three-dimensional rotation group, and the projective two-space in a sequence of recent papers [11-[4]. Many finite-dimensional optimal estimation schemes were obtained mainly due to the closure pro-is that any continuous or bounded-variation p r o b a b i l i t y d e n s i t y on the aforementioned spaces can be wry closely approximated(More)
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