Deterministic learning-based neural identification and knowledge fusion.

Recent deterministic learning methods have achieved locally-accurate identification of unknown system dynamics. However, the locally-accurate identification means that the neural networks can only capture the local dynamics knowledge along the system trajectory. In order to capture a broader knowledge region, this article investigates the knowledge fusion problem of deterministic learning, that is, the integration of different knowledge regions along different individual trajectories. Specifically, two kinds of knowledge fusion schemes are systematically introduced: an online fusion scheme and an offline fusion scheme. The online scheme can be viewed as an extension of distributed cooperative learning control to cooperative neural identification for sampled-data systems. By designing an auxiliary information transmission strategy to enable the neural network to receive information learned from other tasks while learning its own task, it is proven that the weights of all localized RBF networks exponentially converge to their common true/ideal values. The offline scheme can be regarded as a knowledge distillation strategy, in which the fused network is obtained by offline training through the knowledge learned from all individual system trajectories via deterministic learning. A novel weight fusion algorithm with low computational complexity is proposed based on the least squares solution under subspace constraints. Simulation studies show that the proposed fusion schemes can successfully integrate the knowledge regions of different individual trajectories while maintaining the learning performance, thereby greatly expanding the knowledge region learned from deterministic learning.

Rapid Sensor Fault Diagnosis for a Class of Nonlinear Systems via Deterministic Learning.

In this article, a rapid sensor fault diagnosis (SFD) method is presented for a class of nonlinear systems. First, by exploiting the linear adaptive observer technology and the deterministic learning method (DLM), an adaptive neural network (NN) observer is constructed to capture the information of the unknown sensor fault function. Second, when the NN input orbit is a period or recurrent one, the partial persistent excitation (PE) condition of the NNs can be guaranteed through the DLM. Based on the partial PE condition and the uniformly completely observable property of a linear time-varying system, the accurate state estimation and the sensor fault identification can be achieved by properly choosing the observer gain. Third, a bank of dynamical observers utilizing the experiential knowledge is constructed to achieve rapid SFD and data recovery. The attractions of the proposed approach are that accurate approximations of sensor faults can be achieved through the DLM, and the data that are destroyed by the sensor faults can be recovered by using the learning results. Simulation studies of a robot system are utilized to show the effectiveness of the proposed method.

Preparation, stability and commutability of candidate reference materials for lactate dehydrogenase (LDH).

BACKGROUND: Lactate dehydrogenase (LDH) is a key enzyme that functions as a marker of cell damage. Its activity can be measured by a variety of laboratory methods. To eliminate inter-method bias and enhance equivalence among different measurement procedures, LDH detection needs to be standardized. METHODS: Optimized sequences coding for lactate dehydrogenase subunit A (LDH-A) and subunit B (LDH-B) were synthesized and cloned into the pRSFDuet vector, and then the constructed 6His-LDHA-pRSFDuet, 6His-LDHB-pRSFDuet, and 6His-LDHA-LDHB-pRSFDuet plasmids were transformed into Escherichia coli BL21 (DE3) for expression. The enzyme activity and specific activity of recombinant LDHs were detected. Electrophoresis of LDH isoenzymes was performed. The stability of recombinant LDHs and serum LDH was evaluated. Commutability of recombinant LDH-B was studied by the IFCC reference method and six routine methods. RESULTS: Three plasmids were constructed and three highly concentrated recombinant LDH isoenzymes were obtained. The specific activities of LDH-A, LDH-AB, and LDH-B were 18.08 U/mg, 21.74 U/mg, and 14.18 U/mg, respectively. There was a desirable linear correlation between the activities of recombinant LDH isoenzymes and their protein concentrations. Electrophoresis of LDH isoenzymes showed that the recombinant LDH-B corresponded to LDH1 and it demonstrated good stability at 4 °C and 25 °C for 5 weeks. LDH-B formulations in saline-bovine serum albumin solution and human serum matrix were commutable for six routine methods. CONCLUSION: Human recombinant LDH-B has great potential to become an improved and less expensive standard or reference material in external quality assessment for clinical LDH measurement.