2 research outputs found
Boolean Variation and Boolean Logic BackPropagation
The notion of variation is introduced for the Boolean set and based on which
Boolean logic backpropagation principle is developed. Using this concept, deep
models can be built with weights and activations being Boolean numbers and
operated with Boolean logic instead of real arithmetic. In particular, Boolean
deep models can be trained directly in the Boolean domain without latent
weights. No gradient but logic is synthesized and backpropagated through
layers
Construction of adaptive particle swarm optimizers and optimization of parameters in switched dynamical systems
研究成果の概要 (和文) : 様々な問題に柔軟に適応できる粒子群最適化法(PSO)について考察し、3つの新しいPSOを提案した。(1)確定的な差分方程式に支配されるPSO。これは安定性解析や再現性のある動作制御に有利である。(2)粒子の動作を鈍感で接近粒子は衝突するPSO。これは複数解探索に有利である。(3)粒子間の結合にスイッチを導入したPSO。スイッチ頻度にを調節することにより、粒子間の疑似距離の制御や柔軟な粒子間の情報交換が可能である。また、パワーエレクトロニクスへの応用を検討し、スイッチング電源の制御信号最適化や、光電変換系の電源の最大電力点探索への応用の基礎となる成果を得た。研究成果の概要 (英文) : We have studied particle swarm optimizers(PSOs) with flexible adaptation function to various problems and have proposed three novel PSOs. (1) The PSO governed by a deterministic difference equation. It has advantages in analysis of stability and control of reproducible dynamics. (2) The PSO with insensitive particle movement and inter-near-particle collision. It is suitable for multi-solution problems. (3) The PSO with switched inter-particle connection. Adjusting the switching frequency, we can control inter-particle pseudo-distance and can realize flexible inter-particle communication. We have also studied applications to power electronics and have obtained basic results for applications to control signals optimization in switching power converters and to maximum power point search in photovoltaic systems