Weight perturbation learning was proposed as a learning rule in which perturbation is added to the variable parameters of learning machines. The generalization performance of weight perturbation learning was analyzed by statistical mechanical methods and was found to have the same asymptotic generalization property as perceptron learning. In this paper we consider the difference between perceptron learning and AdaTron learning, both of which are well-known learning rules. By applying this difference to weight perturbation learning, we propose adaptive weight perturbation learning. The generalization performance of the proposed rule is analyzed by statistical mechanical methods, and it is shown that the proposed learning rule has an outstanding asymptotic property equivalent to that of AdaTron learning.

AlGaAs/GaAs power HBTs for digital cellular phones have been developed. A three-dimensional thermal analysis taking the local-temperature dependence of the collector current into account was applied to the thermal design of the HBTs. The HBTs were fabricated using the hetero-guardring fully selfaligned transistor technique. The HBT with 220µm2 60 emitters produced a 31.7 dBm CW-output power and 46% poweradded efficiency with an adjacent channel leakage power of -49 dBc at the 50kHz offset bands for a 948 MHz π/4-shifted QPSK modulated signal at a low collector-emitter voltage of 3V. Through comparison with the conventional GaAs power FETs, it has been shown that AlGaAs/GaAs power HBTs have a great advantage in reducing the chip size.